Search results for: real gas model

Authors: Mais Khader, Xingjie Wei

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This research sheds light on female entrepreneurs by providing new insights on the survival predictions of companies led by females in the UK. This study aims to build a predictive machine learning model of the survival of female-led & co-led small & medium enterprises (SMEs) in the UK over the period 2000-2020. The predictive model built utilised a combination of financial and non-financial features related to both companies and their directors to predict SMEs' survival. These features were studied in terms of their contribution to the resultant predictive model. Five machine learning models are used in the modelling: Decision tree, AdaBoost, Naïve Bayes, Logistic regression and SVM. The AdaBoost model had the highest performance of the five models, with an accuracy of 73% and an AUC of 80%. The results show high feature importance in predicting companies' survival for company size, management experience, financial performance, industry, region, and females' percentage in management.

Keywords: company survival, entrepreneurship, females, machine learning, SMEs

Procedia PDF Downloads 97

Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

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This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

Procedia PDF Downloads 120

Authors: Azam Mohammadbagheri, Bahram Fathi

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DEA has become a very popular method of performance measure, but it still suffers from some shortcomings. One of these shortcomings is the issue of having multiple optimal solutions to weights for efficient DMUs. The cross efficiency evaluation as an extension of DEA is proposed to avoid this problem. Lam (2010) is also proposed a mixed-integer linear programming formulation based on linear discriminate analysis and super efficiency method (MILP model) to avoid having multiple optimal solutions to weights. In this study, we modified MILP model to determine more suitable weight sets and also evaluate the energy efficiency of MENA countries as an application of the proposed model.

Keywords: data envelopment analysis, discriminate analysis, cross efficiency, MILP model

Procedia PDF Downloads 685

Authors: Wang Xue, Fan Liwei

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The trust and use of social network platforms by users are instrumental factors that contribute to the platform’s sustainable development. Studying the influential factors of the use of social network platforms is beneficial for developing and maintaining a large user base. This study constructed an extended unified theory of acceptance and use of technology (UTAUT2) moderating model with perceived privacy risks to analyze the factors affecting the trust and use of social network platforms. 444 participants completed our 35 surveys, and we verified the survey results by structural equation model. Empirical results reveal the influencing factors that affect the trust and use of social network platforms, and the extended UTAUT2 model with perceived privacy risks increases the applicability of UTAUT2 in social network scenarios. Social networking platforms can increase their use rate by increasing the economics, functionality, entertainment, and privacy security of the platform.

Keywords: perceived privacy risk, social network, trust, use, UTAUT2 model

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Authors: Edgardo V. Gubatanga Jr., Mark Joshua Salvacion

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Most aerial LiDAR systems have accompanying aerial cameras in order to capture not only the terrain of the surveyed area but also its true-color appearance. However, the presence of atmospheric clouds, poor lighting conditions, and aerial camera problems during an aerial survey may cause absence of aerial photographs. These leave areas having terrain information but lacking aerial photographs. Intensity images can be derived from LiDAR data but they are only grayscale images. A deep learning model is developed to create a complex function in a form of a deep neural network relating the pixel values of LiDAR-derived intensity images and true-color images. This complex function can then be used to predict the true-color images of a certain area using intensity images from LiDAR data. The predicted true-color images do not necessarily need to be accurate compared to the real world. They are only intended to look realistic so that they can be used as base maps.

Keywords: aerial LiDAR, colorization, deep learning, intensity images

Procedia PDF Downloads 158

Authors: Yoon- Ho Shin, Jin-Seung Choi, Dong-Won Kang, Jeong-Woo Seo, Joo-Hack Lee, Ju-Young Kim, Dae-Hyeok Kim, Seung-Tae Yang, Gye-Rae Tack

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It is difficult to study the effect of various variables on cycle fitting through actual experiment. To overcome such difficulty, the forward dynamics of a musculoskeletal model was applied to cycle fitting in this study. The measured EMG data were compared with the muscle activities of the musculoskeletal model through forward dynamics. EMG data were measured from five cyclists who do not have musculoskeletal diseases during three minutes pedaling with a constant load (150 W) and cadence (90 RPM). The muscles used for the analysis were the Vastus Lateralis (VL), Tibialis Anterior (TA), Bicep Femoris (BF), and Gastrocnemius Medial (GM). Person’s correlation coefficients of the muscle activity patterns, the peak timing of the maximum muscle activities, and the total muscle activities were calculated and compared. BIKE3D model of AnyBody (Anybodytech, Denmark) was used for the musculoskeletal model simulation. The comparisons of the actual experiments with the simulation results showed significant correlations in the muscle activity patterns (VL: 0.789, TA: 0.503, BF: 0.468, GM: 0.670). The peak timings of the maximum muscle activities were distributed at particular phases. The total muscle activities were compared with the normalized muscle activities, and the comparison showed about 10% difference in the VL (+10%), TA (+9.7%), and BF (+10%), excluding the GM (+29.4%). Thus, it can be concluded that muscle activities of model & experiment showed similar results. The results of this study indicated that it was possible to apply the simulation of further improved musculoskeletal model to cycle fitting.

Keywords: musculoskeletal modeling, EMG, cycle fitting, simulation

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Authors: Preeda Sansakorn, Min An

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In order to be capable of dealing with uncertainties, subjectivities, including vagueness arising in building construction projects, the application of fuzzy reasoning technique based on fuzzy set theory is proposed. This study contributes significantly to the development of a fuzzy reasoning safety risk assessment model for building construction projects that could be employed to assess the risk magnitude of each hazardous event identified during construction, and a third parameter of probability of consequence is incorporated in the model. By using the proposed safety risk analysis methodology, more reliable and less ambiguities, which provide the safety risk management project team for decision-making purposes.

Keywords: safety risk assessment, building construction safety, fuzzy reasoning, construction risk assessment model, building construction projects

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Authors: Alaa Osman, Sara Elgazzar, Breksal Elmiligy

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The Supply Chain Operation Reference (SCOR) model is considered one of the most widely implemented supply chain performance measurement systems (SCPMSs). Several studies have been proposed on the SCOR model adaptation in developed countries context; while there is a limited availability of previous work on the SCPMSs application generally and the SCOR model specifically in developing nations. This paper presents a research agenda on the SCOR model adaptation in the developing countries. It aims at investigating the challenges of adapting the SCOR model to manage and measure supply chain performance in developing countries. The research will exemplify the system in the Egyptian automotive sector to gain a comprehensive understanding of how the application of the SCOR model can affect the performance of automotive companies in Egypt, with a necessary understanding of challenges and obstacles faced the adaptation of the model in the Egyptian supply chain context. An empirical study was conducted on the Egyptian automotive sector in three companies considering three different classes: BMW, Hyundai and Brilliance. First, in-depth interviews were carried out to gain an insight into the implementation and the relevance of the concepts of supply chain management and performance measurement in the Egyptian automotive industry. Then, a formal survey was designed based on the SCOR model five main processes (plan, source, make, deliver and return) and best practices to investigate the challenges and obstacles faced the adaptation of the SCOR model in the Egyptian automotive supply chain. Finally, based on the survey results, the appropriate best practices for each process were identified in order to overcome the SCOR model adaptation challenges. The results showed that the implementation of the SCOR model faced different challenges and unavailability of the required enablers. The survey highlighted the low integration of end-to-end supply chain, lacks commitment for the innovative ideas and technologies, financial constraints and lack of practical training and support as the main challenges faced the adaptation of the SCOR model in the Egyptian automotive supply chain. The research provides an original contribution to knowledge by proposing a procedure to identify challenges encountered during the process of SCOR model adoption which can pave a way for further research in the area of SCPMSs adaptation, particularly in the developing countries. The research can help managers and organizations to identify obstacles and difficulties of the SCOR model adaptation, subsequently this can facilitate measuring the improved performance or changes in the organizational performance.

Keywords: automotive sector, developing countries, SCOR model, supply chain performance

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Authors: Wajih Abbassi, Zouhaier Ben Khelifa

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The present research points towards the empirical validation of three options valuation models, the ad-hoc Black-Scholes model as proposed by Berkowitz (2001), the constant elasticity of variance model of Cox and Ross (1976) and the Kou jump-diffusion model (2002). Our empirical analysis has been conducted on a sample of 26,974 options written on three indexes, the S&P 500, Nasdaq 100 and the Russell 2000 that were negotiated during the year 2007 just before the sub-prime crisis. We start by presenting the theoretical foundations of the models of interest. Then we use the technique of trust-region-reflective algorithm to estimate the structural parameters of these models from cross-section of option prices. The empirical analysis shows the superiority of the Kou jump-diffusion model. This superiority arises from the ability of this model to portray the behavior of market participants and to be closest to the true distribution that characterizes the evolution of these indices. Indeed the double-exponential distribution covers three interesting properties that are: the leptokurtic feature, the memory less property and the psychological aspect of market participants. Numerous empirical studies have shown that markets tend to have both overreaction and under reaction over good and bad news respectively. Despite of these advantages there are not many empirical studies based on this model partly because probability distribution and option valuation formula are rather complicated. This paper is the first to have used the technique of nonlinear curve-fitting through the trust-region-reflective algorithm and cross-section options to estimate the structural parameters of the Kou jump-diffusion model.

Keywords: jump-diffusion process, Kou model, Leptokurtic feature, trust-region-reflective algorithm, US index options

Procedia PDF Downloads 423

Authors: Ayesha Waqar Khan, Mariam Altaf, Jamil Ahmad, Shaheen Shahzad

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Kidney fibrosis is an anticipated outcome of possibly all types of progressive chronic kidney disease (CKD). Epithelial-mesenchymal transition (EMT) signaling pathway is responsible for production of matrix-producing fibroblasts and myofibroblasts in diseased kidney. In this study, a discrete model of TGF-beta (transforming growth factor) and CTGF (connective tissue growth factor) was constructed using Rene Thomas formalism to investigate renal fibrosis turn over. The kinetic logic proposed by Rene Thomas is a renowned approach for modeling of Biological Regulatory Networks (BRNs). This modeling approach uses a set of constraints which represents the dynamics of the BRN thus analyzing the pathway and predicting critical trajectories that lead to a normal or diseased state. The molecular connection between TGF-beta, Smad 2/3 (transcription factor) phosphorylation and CTGF is modeled using GenoTech. The order of BRN is CTGF, TGF-B, and SMAD3 respectively. The predicted cycle depicts activation of TGF-B (TGF-β) via cleavage of its own pro-domain (0,1,0) and presentation to TGFR-II receptor phosphorylating SMAD3 (Smad2/3) in the state (0,1,1). Later TGF-B is turned off (0,0,1) thereby activating SMAD3 that further stimulates the expression of CTGF in the state (1,0,1) and itself turns off in (1,0,0). Elevated CTGF expression reactivates TGF-B (1,1,0) and the cycle continues. The predicted model has generated one cycle and two steady states. Cyclic behavior in this study represents the diseased state in which all three proteins contribute to renal fibrosis. The proposed model is in accordance with the experimental findings of the existing diseased state. Extended cycle results in enhanced CTGF expression through Smad2/3 and Smad4 translocation in the nucleus. The results suggest that the system converges towards organ fibrogenesis if CTGF remains constructively active along with Smad2/3 and Smad 4 that plays an important role in kidney fibrosis. Therefore, modeling regulatory pathways of kidney fibrosis will escort to the progress of therapeutic tools and real-world useful applications such as predictive and preventive medicine.

Keywords: CTGF, renal fibrosis signaling pathway, system biology, qualitative modeling

Procedia PDF Downloads 176

Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

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we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

Procedia PDF Downloads 137

Authors: Roman J. Jedrzejczyk, Damian K. Chlebda, Anna Dziedzicka, Rafal Wazny, Agnieszka Domka, Maciej Sitarz, Przemyslaw J. Jodlowski

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The aim of this study was to obtain antimicrobial material based on thin zirconium dioxide coatings on structured reactors doped with metal nanoparticles using the sonochemical sol-gel method. As a result, dense, uniform zirconium dioxide films were obtained on the kanthal sheets which can be used as support materials in antimicrobial converters with sophisticated shapes. The material was characterised by physicochemical methods, such as AFM, SEM, EDX, XRF, XRD, XPS and in situ Raman and DRIFT spectroscopy. In terms of antimicrobial activity, the material was tested by ATP/AMP method using model microbes isolated from the real systems. The results show that the material can be potentially used in the market as a good candidate for active package and as active bulkheads of climatic systems. The mechanical tests showed that the developed method is an efficient way to obtain durable converters with high antimicrobial activity against fungi and bacteria.

Keywords: antimicrobial properties, kanthal steel, nanocomposite, zirconium oxide

Procedia PDF Downloads 197

Authors: Rahul Paliwal, Gulshan Maheshwari, Anant S. Jhaveri, Channamallikarjun S. Mathpati

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Large pumping facility is the necessary requirement of the cooling water systems for power plants, process and manufacturing facilities, flood control and water or waste water treatment plant. With a large capacity of few hundred to 50,000 m3/hr, cares must be taken to ensure the uniform flow to the pump to limit vibration, flow induced cavitation and performance problems due to formation of air entrained vortex and swirl flow. Successful prediction of these phenomena requires numerical method and turbulence model to characterize the dynamics of these flows. In the past years, single phase shear stress transport (SST) Reynolds averaged Navier Stokes Models (like k-ε, k-ω and RSM) were used to predict the behavior of flow. Literature study showed that two phase model will be more accurate over single phase model. In this paper, a 3D geometries simulated using detached eddy simulation (LES) is used to predict the behavior of the fluid and the results are compared with experimental results. Effect of different grid structure and boundary condition is also studied. It is observed that two phase flow model can more accurately predict the mean flow and turbulence statistics compared to the steady SST model. These validate model will be used for further analysis of vortex structure in lab scale model to generate their frequency-plot and intensity at different location in the set-up. This study will help in minimizing the ill effect of vortex on pump performance.

Keywords: grid structure, pump intake, simulation, vibration, vortex

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Authors: Paul Kenneth D. Luzon, Maria Antonia N. Tanchuling

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The Quezon City Controlled Dumping facility also known as Payatas produces leachate which can contaminate soil and water environment in the area. The goal of this study is to quantify the leachate produced by the QCCDF using the Hydrologic Evaluation of Landfill Performance (HELP) model. Results could be used as input for groundwater contaminant transport studies. The HELP model is based on a simple water budget and is an essential “model requirement” used by the US Environmental Protection Agency (EPA). Annual waste profile of the QCCDF was calculated. Based on topographical maps and estimation of settlement due to overburden pressure and degradation, a total of 10M m^3 of waste is contained in the landfill. The input necessary for the HELP model are weather data, soil properties, and landfill design. Results showed that from 1988 to 2011, an average of 50% of the total precipitation percolates through the bottom layer. Validation of the results is still needed due to the assumptions made in the study. The decrease in porosity of the top soil cover showed the best mitigation for minimizing percolation rate. This study concludes that there is a need for better leachate management system in the QCCDF.

Keywords: help model, landfill, payatas trash slide, quezon city controlled dumping facility

Procedia PDF Downloads 287

Authors: Fadi Al Khatib, Raouf Mbarki, Malek Adouni

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In various sport and recreational activities, the patellofemoral joint undergoes large forces and moments while accommodating the significant knee joint movement. In doing so, this joint is commonly the source of anterior knee pain related to instability in normal patellar tracking and excessive pressure syndrome. One well-observed explanation of the instability of the normal patellar tracking is the patellofemoral ligaments and patellar tendon damage. Improved knowledge of the damage mechanism mediating ligaments and tendon injuries can be a great help not only in rehabilitation and prevention procedures but also in the design of better reconstruction systems in the management of knee joint disorders. This damage mechanism, specifically due to excessive mechanical loading, has been linked to the micro level of the fibred structure precisely to the tropocollagen molecules and their connection density. We argue defining a clear frame starting from the bottom (micro level) to up (macro level) in the hierarchies of the soft tissue may elucidate the essential underpinning on the state of the ligaments damage. To do so, in this study a multiscale fibril reinforced hyper elastoplastic Finite Element model that accounts for the synergy between molecular and continuum syntheses was developed to determine the short-term stresses/strains patellofemoral ligaments and tendon response. The plasticity of the proposed model is associated only with the uniaxial deformation of the collagen fibril. The yield strength of the fibril is a function of the cross-link density between tropocollagen molecules, defined here by a density function. This function obtained through a Coarse-graining procedure linking nanoscale collagen features and the tissue level materials properties using molecular dynamics simulations. The hierarchies of the soft tissues were implemented using the rule of mixtures. Thereafter, the model was calibrated using a statistical calibration procedure. The model then implemented into a real structure of patellofemoral ligaments and patellar tendon (OpenKnee) and simulated under realistic loading conditions. With the calibrated material parameters the calculated axial stress lies well with the experimental measurement with a coefficient of determination (R2) equal to 0.91 and 0.92 for the patellofemoral ligaments and the patellar tendon respectively. The ‘best’ prediction of the yielding strength and strain as compared with the reported experimental data yielded when the cross-link density between the tropocollagen molecule of the fibril equal to 5.5 ± 0.5 (patellofemoral ligaments) and 12 (patellar tendon). Damage initiation of the patellofemoral ligaments was located at the femoral insertions while the damage of the patellar tendon happened in the middle of the structure. These predicted finding showed a meaningful correlation between the cross-link density of the tropocollagen molecules and the stiffness of the connective tissues of the extensor mechanism. Also, damage initiation and propagation were documented with this model, which were in satisfactory agreement with earlier observation. To the best of our knowledge, this is the first attempt to model ligaments from the bottom up, predicted depending to the tropocollagen cross-link density. This approach appears more meaningful towards a realistic simulation of a damaging process or repair attempt compared with certain published studies.

Keywords: tropocollagen, multiscale model, fibrils, knee ligaments

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Authors: Muhammad Anwar, Ameen Ullah, Intakhab Alam Qadri

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This study presents a technique utilizing the Iterative Rational Krylov Algorithm (IRKA) to reduce the order of large-scale descriptor systems. Descriptor systems, which incorporate differential and algebraic components, pose unique challenges in Model Order Reduction (MOR). The proposed method partitions the descriptor system into polynomial and strictly proper parts to minimize approximation errors, applying IRKA exclusively to the strictly adequate component. This approach circumvents the unbounded errors that arise when IRKA is directly applied to the entire system. A comparative analysis demonstrates the high accuracy of the reduced model and a significant reduction in computational burden. The reduced model enables more efficient simulations and streamlined controller designs. The study highlights IRKA-based MOR’s effectiveness in optimizing complex systems’ performance across various engineering applications. The proposed methodology offers a promising solution for reducing the complexity of large-scale descriptor systems while maintaining their essential characteristics and facilitating their analysis, simulation, and control design.

Keywords: model order reduction, descriptor systems, iterative rational Krylov algorithm, interpolatory model reduction, computational efficiency, projection methods, H₂-optimal model reduction

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Authors: Tigist Mekonnen Melesse

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Since rural households are basically entitled to food through own production, improving productivity might lead to enhance the welfare of rural population through higher food availability at the household level and lowering the price of agricultural products. Increasing agricultural productivity through the use of improved technology is one of the desired outcomes from sensible food security and agricultural policy. The ultimate objective of this study was to evaluate the potential impact of improved agricultural technology adoption on smallholders’ crop productivity and welfare. The study is conducted in Ethiopia covering 1500 rural households drawn from four regions and 15 rural villages based on data collected by Ethiopian Rural Household Survey. Endogenous treatment effect model is employed in order to account for the selection bias on adoption decision that is expected from the self-selection of households in technology adoption. The treatment indicator, technology adoption is a binary variable indicating whether the household used improved seeds and chemical fertilizer or not. The outcome variables were cereal crop productivity, measured in real value of production and welfare of households, measured in real per capita consumption expenditure. Results of the analysis indicate that there is positive and significant effect of improved technology use on rural households’ crop productivity and welfare in Ethiopia. Adoption of improved seeds and chemical fertilizer alone will increase the crop productivity by 7.38 and 6.32 percent per year of each. Adoption of such technologies is also found to improve households’ welfare by 1.17 and 0.25 percent per month of each. The combined effect of both technologies when adopted jointly is increasing crop productivity by 5.82 percent and improving welfare by 0.42 percent. Besides, educational level of household head, farm size, labor use, participation in extension program, expenditure for input and number of oxen positively affect crop productivity and household welfare, while large household size negatively affect welfare of households. In our estimation, the average treatment effect of technology adoption (average treatment effect on the treated, ATET) is the same as the average treatment effect (ATE). This implies that the average predicted outcome for the treatment group is similar to the average predicted outcome for the whole population.

Keywords: Endogenous treatment effect, technologies, productivity, welfare, Ethiopia

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Authors: Salim Ahmed

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In many countries, water pollution from industrial effluents is a real problem. It may have a negative impact on the environment. To minimize the adverse effects of these contaminants, various methods are used to improve effluent purification, including physico-chemical processes such as adsorption.The present study focuses on applying a naturally biodegradable adsorbent based on argan (southern Morocco) in a physico-chemical adsorption process to reduce the harmful effects of pollutants on the environment. Tests were carried out with the cationic dye methylene blue (MB) and revealed that removal is significantly higher within the first 15 minutes. The parameters studied in this study are adsorbent mass and concentration. The Freundlich model provides an excellent example of the adsorption phenomenon of BMs over argan powder. The results of this study show that argan kernels are a highly beneficial alternative for local communities, as they help to achieve a triple objective: pollution reduction, waste recovery and water recycling.

Keywords: environmental protection, activated carbon, water treatment, adsorption

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Authors: Zainab Ajab Mohideen, Kiran Kaur, A. Basheer Ahamadhu, Noor Azlinda Wan Jan, Sukmawati Muhammad

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The corpus in the digital library is to provide an overview and evidence from library automation that can be used to justify the needs of the digital library. This paper disperses the approach and implementation of the digital library as part of best practices by the Automation Division at Hamzah Sendut Library of the University Science Malaysia (USM). The implemented digital library model emphasizes on the entire library collections, technical perspective, and automation solution. This model served as a foundation for digital library services as part of information delivery in the USM digital library. The approach to digital library includes discussion on key factors, design, architecture, and pragmatic model that has been collected, captured, and identified during the implementation stages. At present, the USM digital library has achieved the status of an Institutional Repository (IR).

Keywords: academic digital library, digital information system, digital library best practice, digital library model

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Authors: Hao Hsi Tseng

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Taiwan’s infrastructure is gradually deteriorating, while resources for maintenance and replacement are increasingly limited, raising the urgent need for methods for maintaining existing infrastructure within constrained budgets. Infrastructure value evaluation is used to enhance the efficiency of infrastructure maintenance work, allowing administrators to quickly assess the maintenance needs and performance by observing variation in infrastructure value. This research establishes a value evaluation model for Taiwan’s highway box girder bridges. The operating mechanism and process of the model are illustrated in a practical case.

Keywords: box girder bridge, deterioration, infrastructure, maintenance, value evaluation

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Authors: Jae Hyun Lee

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Manufacturing companies use life-cycle inventory databases to analyze sustainability of their manufacturing processes. Life cycle inventory data provides reference data which may not be accurate for a specific company. Collecting accurate data of manufacturing processes for a specific company requires enormous time and efforts. An information model of typical manufacturing processes can reduce time and efforts to get appropriate reference data for a specific company. This paper shows an attempt to build an abstract information model which can be used to develop information models for specific manufacturing processes.

Keywords: process information model, sustainability, OWL, manufacturing

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Authors: Alan C. Lin, Tran Anh Son

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This study proposes an approach to determine the undercut regions and their releasing directions for slider design of complex parts represented by the file format of STL (STereoLithography). In order to delineate the border of undercut regions, orthogonal cutting planes are firstly employed to automatically find the inner loops of a part model. To discover the facets belonging to undercut regions, attributes are then assigned to the facets of the part model based on the topological relationship of adjacent facets of each inner loop. After that, the undercut regions are separated from other facets in the model. Through the recognized facets of the undercut regions, the concept of 'visibility map (V-map)' is further applied to determine feasible releasing directions for each of the undercut regions. The undercut regions having the same releasing direction are finally grouped to form a slider in the injection mold.

Keywords: solid model, STL data, injection mold design, visibility map

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Authors: Jeelka Ray

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Survey of the papers revealed that there is no practical design available for packaging machine based on Embedded system, so the need arose for the development of the prototype model. In this paper, author has worked on the development of an ARM7 based Embedded Controller for controlling the sequence of packaging machine. The unit is made user friendly with TFT and Touch Screen implementing human machine interface (HMI). The different system components are briefly discussed, followed by a description of the overall design. The major functions which involve bag forming, sealing temperature control, fault detection, alarm, animated view on the home screen when the machine is working as per different parameters set makes the machine performance more successful. LPC2478 ARM 7 Embedded Microcontroller controls the coordination of individual control function modules. In back gone days, these machines were manufactured with mechanical fittings. Later on, the electronic system replaced them. With the help of ongoing technologies, these mechanical systems were controlled electronically using Microprocessors. These became the backbone of the system which became a cause for the updating technologies in which the control was handed over to the Microcontrollers with Servo drives for accurate positioning of the material. This helped to maintain the quality of the products. Including all, RS 485 MODBUS Communication technology is used for synchronizing AC Drive & Servo Drive. These all concepts are operated either manually or through a Graphical User Interface. Automatic tuning of heaters, sealers and their temperature is controlled using Proportional, Integral and Derivation loops. In the upcoming latest technological world, the practical implementation of the above mentioned concepts is really important to be in the user friendly environment. Real time model is implemented and tested on the actual machine and received fruitful results.

Keywords: packaging machine, embedded system, ARM 7, micro controller, HMI, TFT, touch screen, PID

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Authors: Ebrahim Ghasemi Ardi, Ai Bing Yu, Run Yu Yang

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Current study is aimed to develop an available in-house discrete element method (DEM) code and link it with direct breakage event. So, it became possible to determine the particle breakage and then its fragments size distribution, simultaneous with DEM simulation. It directly applies the particle breakage inside the DEM computation algorithm and if any breakage happens the original particle is replaced with daughters. In this way, the calculation will be followed based on a new updated particles list which is very similar to the real grinding environment. To validate developed model, a grinding ball impacting an unconfined particle bed was simulated. Since considering an entire ball mill would be too computationally demanding, this method provided a simplified environment to test the model. Accordingly, a representative volume of the ball mill was simulated inside a box, which could emulate media (ball)–powder bed impacts in a ball mill and during particle bed impact tests. Mono, binary and ternary particle beds were simulated to determine the effects of granular composition on breakage kinetics. The results obtained from the DEM simulations showed a reduction in the specific breakage rate for coarse particles in binary mixtures. The origin of this phenomenon, commonly known as cushioning or decelerated breakage in dry milling processes, was explained by the DEM simulations. Fine particles in a particle bed increase mechanical energy loss, and reduce and distribute interparticle forces thereby inhibiting the breakage of the coarse component. On the other hand, the specific breakage rate of fine particles increased due to contacts associated with coarse particles. Such phenomenon, known as acceleration, was shown to be less significant, but should be considered in future attempts to accurately quantify non-linear breakage kinetics in the modeling of dry milling processes.

Keywords: particle bed, breakage models, breakage kinetic, discrete element method

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Authors: Abdurrasheed Sa'id, Abubakar Isma'il, Waheed Alayande

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This study focused on carrying out numerical simulations of Kangimi reservoir sedimentation by reviewing different numerical sediment transport models, and GSTARS3 was selected. The model was developed using the 1977 data. It was calibrated by simulating the 2012 profile and sediment deposition and compared with 2012 hydrographic survey results of NWRI. The model was validated by simulating the 2016 deposition and compared the results with NWRI estimates. Also, the performance of the proposed model was tested using statistical parameters such as MSE (Mean Square Error), MAPE (Mean Average Percentage Error) and R2 (Coefficient of determination) with values of 1.32m, 0.17% and 0.914 respectively which shows strong agreement. After the calibration, validation and performance testing the model was used to simulate the 2032 and 2062 profiles and deposition. The results showed that by 2032 the reservoir will be silted by 25.34MCM or 43.3% of the design capacity and 60.7% of the capacity by the year 2062. A number of sedimentation mitigation measures were recommended.

Keywords: NWRI- national water resources institute, sedimentation, GSTARS3, model

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Authors: Hyun-Woo Cho

Abstract:

Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Jingsi Li, Neil S. Ferguson

Abstract:

Time pressure could have an influence on the productivity, quality of decision making, and the efficiency of problem-solving. This has been mostly stemmed from cognitive research or psychological literature. However, a salient scarce discussion has been held for transport adjacent fields. It is conceivable that in many activity-travel contexts, time pressure is a potentially important factor since an excessive amount of decision time may incur the risk of late arrival to the next activity. The activity-travel rescheduling behavior is commonly explained by costs and benefits of factors such as activity engagements, personal intentions, social requirements, etc. This paper hypothesizes that an additional factor of perceived time pressure could affect travelers’ rescheduling behavior, thus leading to an impact on travel demand management. Time pressure may arise from different ways and is assumed here to be essentially incurred due to travelers planning their schedules without an expectation of unforeseen elements, e.g., transport disruption. In addition to a linear-additive utility-maximization model, the less computationally compensatory heuristic models are considered as an alternative to simulate travelers’ responses. The paper will contribute to travel behavior modeling research by investigating the following questions: how to measure the time pressure properly in an activity-travel day plan context? How do travelers reschedule their plans to cope with the time pressure? How would the importance of the activity affect travelers’ rescheduling behavior? What will the behavioral model be identified to describe the process of making activity-travel rescheduling decisions? How do these identified coping strategies affect the transport network? In this paper, a Mixed Heuristic Model (MHM) is employed to identify the presence of different choice heuristics through a latent class approach. The data about travelers’ activity-travel rescheduling behavior is collected via a web-based interactive survey where a fictitious scenario is created comprising multiple uncertain events on the activity or travel. The experiments are conducted in order to gain a real picture of activity-travel reschedule, considering the factor of time pressure. The identified behavioral models are then integrated into a multi-agent transport simulation model to investigate the effect of the rescheduling strategy on the transport network. The results show that an increased proportion of travelers use simpler, non-compensatory choice strategies instead of compensatory methods to cope with time pressure. Specifically, satisfying - one of the heuristic decision-making strategies - is adopted commonly since travelers tend to abandon the less important activities and keep the important ones. Furthermore, the importance of the activity is found to increase the weight of negative information when making trip-related decisions, especially route choices. When incorporating the identified non-compensatory decision-making heuristic models into the agent-based transport model, the simulation results imply that neglecting the effect of perceived time pressure may result in an inaccurate forecast of choice probability and overestimate the affectability to the policy changes.

Keywords: activity-travel rescheduling, decision making under uncertainty, mixed heuristic model, perceived time pressure, travel demand management

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Authors: Suttipong Boonphadung, Thassanant Unnanantn

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The research study aimed to (1) compare the critical thinking of the teacher students of Suan Sunandha Rajabhat University before and after applying Miller’s Model learning activities and (2) investigate the students’ opinions towards Miller’s Model learning activities for improving the critical thinking. The participants of this study were purposively selected. They were 3 groups of teacher students: (1) fourth year 33 student teachers majoring in Early Childhood Education and enrolling in semester 1 of academic year 2013 (2) third year 28 student teachers majoring in English and enrolling in semester 2 of academic year 2013 and (3) third year 22 student teachers majoring in Thai and enrolling in semester 2 of academic year 2013. The research instruments were (1) lesson plans where the learning activities were settled based on Miller’s Model (2) critical thinking assessment criteria and (3) a questionnaire on opinions towards Miller’s Model based learning activities. The statistical treatment was mean, deviation, different scores and T-test. The result unfolded that (1) the critical thinking of the students after the assigned activities was better than before and (2) the students’ opinions towards the critical thinking improvement activities based on Miller’s Model ranged from the level of high to highest.

Keywords: critical thinking, Miller’s model, opinions, pre-service teachers

Procedia PDF Downloads 475

Authors: B. O. Olawale, C. R. Chatwin, R. C. D. Young, P. M. Birch, F. O. Faithpraise, A. O. Olukiran

Abstract:

Ortho-rectification is the process of geometrically correcting an aerial image such that the scale is uniform. The ortho-image formed from the process is corrected for lens distortion, topographic relief, and camera tilt. This can be used to measure true distances, because it is an accurate representation of the Earth’s surface. Ortho-rectification and geo-referencing are essential to pin point the exact location of targets in video imagery acquired at the UAV platform. This can only be achieved by comparing such video imagery with an existing digital map. However, it is only when the image is ortho-rectified with the same co-ordinate system as an existing map that such a comparison is possible. The video image sequences from the UAV platform must be geo-registered, that is, each video frame must carry the necessary camera information before performing the ortho-rectification process. Each rectified image frame can then be mosaicked together to form a seamless image map covering the selected area. This can then be used for comparison with an existing map for geo-referencing. In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) Decompilation of video stream into individual frames; (2) Finding of interior camera orientation parameters; (3) Finding the relative exterior orientation parameters for each video frames with respect to each other; (4) Finding the absolute exterior orientation parameters, using self-calibration adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a 2-D planimetric mapping, which can be compared with a well referenced existing digital map for the purpose of georeferencing and aerial surveillance. A test field located in Abuja, Nigeria was used for testing our method. Fifteen minutes video and telemetry data were collected using the UAV and the data collected were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images are more reliable than those from original perspective photographs when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 meters.

Keywords: geo-referencing, ortho-rectification, video frame, self-calibration

Procedia PDF Downloads 477

Authors: Adewole A. Ayoade, Marshall R. Sweatt, John P. H. Steele, Qi Han, Khaled Al-Wahedi, Hamad Karki, William A. Yearsley

Abstract:

Understanding the behavioral characteristics of sensors is a crucial step in fusing data from several sensors of different types. This paper introduces a practical, real-time approach to integrate heterogeneous sensor data to achieve higher accuracy than would be possible from any one individual sensor in localizing a mobile robot. We use this approach in both indoor and outdoor environments and it is especially appropriate for those environments like oil and gas refineries due to their sparse and featureless nature. We have studied the individual contribution of each sensor data to the overall combined accuracy achieved from the fusion process. A Sequential Update Extended Kalman Filter(EKF) using validation gates was used to integrate GPS data, Compass data, WiFi data, Inertial Measurement Unit(IMU) data, Vehicle Velocity, and pose estimates from Fiducial marker system. Results show that the approach can enable a mobile robot to navigate autonomously in any environment using a priori information.

Keywords: inspection mobile robot, navigation, sensor fusion, sequential update extended Kalman filter

Procedia PDF Downloads 469