Search results for: cluster model approach

Authors: D. S. Zhuravkova, A. G. Temnikov, O. S. Belova, L. L. Chernensky, T. K. Gerastenok, I. Y. Kalugina, N. Y. Lysov, A.V. Orlov

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Investigation of the discharges from the artificial charged water aerosol clouds in presence of the arrays of the model hydrometeors could help to receive the new data about the peculiarities of the return stroke formation between the thundercloud and the ground when the large volumes of the hail particles participate in the lightning discharge initiation and propagation stimulation. Artificial charged water aerosol clouds of the negative or positive polarity with the potential up to one million volts have been used. Hail has been simulated by the group of the conductive model hydrometeors of the different form. Parameters of the impulse current of the main stage of the discharge between the artificial positively and negatively charged water aerosol clouds and the ground in presence of the model hydrometeors array and of its corresponding electromagnetic radiation have been determined. It was established that the parameters of the array of the model hydrometeors influence on the parameters of the main stage of the discharge between the artificial thundercloud cell and the ground. The maximal values of the main stage current impulse parameters and the electromagnetic radiation registered by the plate antennas have been found for the array of the model hydrometeors of the cylinder revolution form for the negatively charged aerosol cloud and for the array of the hydrometeors of the plate rhombus form for the positively charged aerosol cloud, correspondingly. It was found that parameters of the main stage of the discharge between the artificial charged water aerosol cloud and the ground in presence of the model hydrometeor array of the different considered forms depend on the polarity of the artificial charged aerosol cloud. In average, for all forms of the investigated model hydrometeors arrays, the values of the amplitude and the current rise of the main stage impulse current and the amplitude of the corresponding electromagnetic radiation for the artificial charged aerosol cloud of the positive polarity were in 1.1-1.9 times higher than for the charged aerosol cloud of the negative polarity. Thus, the received results could indicate to the possible more important role of the big volumes of the large hail arrays in the thundercloud on the parameters of the return stroke for the positive lightning.

Keywords: main stage of discharge, hydrometeor form, lightning parameters, negative and positive artificial charged aerosol cloud

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Authors: Pratch Kittipongpattana, Thongchai Fongsamootr

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This research aimed to study the influences of a soot blowing operation and geometrical variables to the stress characteristic of water wall tubes located in soot blowing areas which caused the boilers of Mae Moh power plant to lose their generation hour. The research method is divided into 2 parts (a) measuring the strain on water wall tubes by using 3-element rosette strain gages orientation during a full capacity plant operation and in periods of soot blowing operations (b) creating a finite element model in order to calculate stresses on tubes and validating the model by using experimental data in a steady state plant operation. Then, the geometrical variables in the model were changed to study stresses on the tubes. The results revealed that the stress was not affected by the soot blowing process and the finite element model gave the results 1.24% errors from the experiment. The geometrical variables influenced the stress, with the most optimum tubes design in this research reduced the average stress from the present design 31.28%.

Keywords: boiler water wall tube, finite element, stress analysis, strain gage rosette

Procedia PDF Downloads 389

Authors: R. Chanajaree, D. Luanwiset, K. Pongpratea

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Dye removal is an environmental concern because the textile industries have been increasing by world population and industrialization. Adsorption is the technique to find adsorbents to remove dyes from wastewater. This method is low-cost and effective for dye removal. This work tries to develop effective adsorbents using the computational approach because it will be able to predict the possibility of the adsorbents for specific dyes in terms of binding free energies. The computational approach is faster and cheaper than the experimental approach in case of finding the best adsorbents. All starting structures of dyes and adsorbents are optimized by quantum calculation. The complexes between dyes and adsorbents are generated by the docking method. The obtained binding free energies from docking are compared to binding free energies from the experimental data. The calculated energies can be ranked as same as the experimental results. In addition, this work also shows the possible orientation of the complexes. This work used two experimental groups of the complexes of the dyes and adsorbents. In the first group, there are chitosan (adsorbent) and two dyes (reactive red (RR) and direct sun yellow (DY)). In the second group, there are poly(1,2-epoxy-3-phenoxy) propane (PEPP), which is the adsorbent, and 2 dyes of bromocresol green (BCG) and alizarin yellow (AY).

Keywords: dyes removal, binding free energies, quantum calculation, docking

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Authors: C. S. Subhashini, H. L. Premaratne

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Landslides are the most recurrent and prominent disaster in Sri Lanka. Sri Lanka has been subjected to a number of extreme landslide disasters that resulted in a significant loss of life, material damage, and distress. It is required to explore a solution towards preparedness and mitigation to reduce recurrent losses associated with landslides. Artificial Neural Networks (ANNs) and Hidden Markov Model (HMMs) are now widely used in many computer applications spanning multiple domains. This research examines the effectiveness of using Artificial Neural Networks and Hidden Markov Model in landslides predictions and the possibility of applying the modern technology to predict landslides in a prominent geographical area in Sri Lanka. A thorough survey was conducted with the participation of resource persons from several national universities in Sri Lanka to identify and rank the influencing factors for landslides. A landslide database was created using existing topographic; soil, drainage, land cover maps and historical data. The landslide related factors which include external factors (Rainfall and Number of Previous Occurrences) and internal factors (Soil Material, Geology, Land Use, Curvature, Soil Texture, Slope, Aspect, Soil Drainage, and Soil Effective Thickness) are extracted from the landslide database. These factors are used to recognize the possibility to occur landslides by using an ANN and HMM. The model acquires the relationship between the factors of landslide and its hazard index during the training session. These models with landslide related factors as the inputs will be trained to predict three classes namely, ‘landslide occurs’, ‘landslide does not occur’ and ‘landslide likely to occur’. Once trained, the models will be able to predict the most likely class for the prevailing data. Finally compared two models with regards to prediction accuracy, False Acceptance Rates and False Rejection rates and This research indicates that the Artificial Neural Network could be used as a strong decision support system to predict landslides efficiently and effectively than Hidden Markov Model.

Keywords: landslides, influencing factors, neural network model, hidden markov model

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Authors: Alfred Hasanaj , Ardit Gjeta, Miranda Kullolli

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The approach in analyzing defects on different pipe lines is conducted through Failure Assessment Diagram (FAD). These methods of analyses have further extended in recent years. This approach is used to identify and stress out a solution for the defects which randomly occur with gas pipes such are corrosion defects, gauge defects, and combination of defects where gauge and dents are included. Few of the defects are to be analyzed in this paper where our main focus will be the fracture of cast Iron pipes, elastic-plastic failure and plastic collapse of X52 steel pipes for gas transport. We need to conduct a calculation of probability of the defects in order to predict and avoid such costly defects.

Keywords: defects, failure assessment diagrams, steel pipes, safety factor

Procedia PDF Downloads 445

Authors: Subhankar Karmakar, Madhan Kumar Vasudevan, Manivannan Muniyandi

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Thermal Grill Illusion (TGI) elicits a strong and often painful sensation of burn when interlacing warm and cold stimuli that are individually non-painful, excites thermoreceptors beneath the skin. Among several theories of TGI, the “disinhibition” theory is the most widely accepted in the literature. According to this theory, TGI is the result of the disinhibition or unmasking of the pain-sensitive HPC (Heat-Pinch-Cold) nerve fibers due to the inhibition of cold-sensitive nerve fibers that are responsible for masking HPC nerve fibers. Although researchers focused on understanding TGI throughexperiments and models, none of them investigated the prediction of TGI pain intensity through a computational model. Furthermore, the comparison of psychophysically perceived TGI intensity with neurophysiological models has not yet been studied. The prediction of pain intensity through a computational model of TGI can help inoptimizing thermal displays and understanding pathological conditions related to temperature perception. The current studyfocuses on developing a computational model to predict the intensity of TGI pain and experimentally observe the perceived TGI pain. The computational model is developed based on the disinhibition theory and by utilizing the existing popular models of warm and cold receptors in the skin. The model aims to predict the neuronal activity of the HPC nerve fibers. With a temperature-controlled thermal grill setup, fifteen participants (ten males and five females) were presented with five temperature differences between warm and cold grills (each repeated three times). All the participants rated the perceived TGI pain sensation on a scale of one to ten. For the range of temperature differences, the experimentally observed perceived intensity of TGI is compared with the neuronal activity of pain-sensitive HPC nerve fibers. The simulation results show a monotonically increasing relationship between the temperature differences and the neuronal activity of the HPC nerve fibers. Moreover, a similar monotonically increasing relationship is experimentally observed between temperature differences and the perceived TGI intensity. This shows the potential comparison of TGI pain intensity observed through the experimental study with the neuronal activity predicted through the model. The proposed model intends to bridge the theoretical understanding of the TGI and the experimental results obtained through psychophysics. Further studies in pain perception are needed to develop a more accurate version of the current model.

Keywords: thermal grill Illusion, computational modelling, simulation, psychophysics, haptics

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Authors: Sylvanus N. Wosu

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Research shows that factors, such as low motivation, preparation, resources, emotional and social integration, and fears of risk-taking, are the most common barriers to access, matriculation, and retention into science, technology, engineering, and mathematics (STEM) disciplines for underrepresented (URM) students. These factors have been shown to impact students’ attraction and success in STEM fields. Standardized tests such as the SAT and ACT often used as predictor of success, are not always true predictors of success for African and Hispanic American students. Without an adequate academic support environment, even a high SAT score does not guarantee academic success in science and engineering. This paper proposes a model for Academic Coaching for building success and inclusive excellence in STEM education. Academic coaching is framed as a process of motivating students to be independent learners through relational mentorship, facilitating learning supports inside and outside of the classroom or school environment, and developing problem-solving skills and success attitudes that lead to higher performance in the specific subjects. The model is formulated based on best strategies and practices for enriching Academic Performance Impact skills and motivating students’ interests in STEM. A scaled model for measuring the Academic Performance Impact (API) index and STEM is discussed. The study correlates API with state standardized test and shows that the average impact of those skills can be predicted by the Academic Performance Impact (API) index or Academic Preparedness Index.

Keywords: diversity, equity, graduate education, inclusion, inclusive excellence, model

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Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

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Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Edvard P. G. Bruun

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One of the best ways of improving our understanding of reinforced concrete is through large-scale experimental testing. The gathered information is critical in making inferences about structural mechanics and deriving the mathematical models that are the basis for finite element analysis programs and design codes. An effective way of measuring the strains across a region of a specimen is by using a system of surface mounted Linear Variable Differential Transformers (LVDTs). While a single LVDT can only measure the linear strain in one direction, by combining several measurements at known angles a Mohr’s circle of strain can be derived for the whole region under investigation. This paper presents a method that can be used by researchers, which improves the accuracy and removes experimental bias in the calculation of the Mohr’s circle, using four rather than three independent strain measurements. Obtaining high quality strain data is essential, since knowing the angular deviation (shear strain) and the angle of principal strain in the region are important properties in characterizing the governing structural mechanics. For example, the Modified Compression Field Theory (MCFT) developed at the University of Toronto, is a rotating crack model that requires knowing the direction of the principal stress and strain, and then calculates the average secant stiffness in this direction. But since LVDTs can only measure average strains across a plane (i.e., between discrete points), localized cracking and spalling that typically occur in reinforced concrete, can lead to unrealistic results. To build in redundancy and improve the quality of the data gathered, the typical experimental setup for a large-scale shell specimen has four independent directions (X, Y, H, and V) that are instrumented. The question now becomes, which three should be used? The most common approach is to simply discard one of the measurements. The problem is that this can produce drastically different answers, depending on the three strain values that are chosen. To overcome this experimental bias, and to avoid simply discarding valuable data, a more rigorous approach would be to somehow make use of all four measurements. This paper presents the derivation of a method to draw what is effectively a Mohr’s circle of 'best-fit', which optimizes the circle by using all four independent strain values. The four-strain optimized Mohr’s circle approach has been utilized to process data from recent large-scale shell tests at the University of Toronto (Ruggiero, Proestos, and Bruun), where analysis of the test data has shown that the traditional three-strain method can lead to widely different results. This paper presents the derivation of the method and shows its application in the context of two reinforced concrete shells tested in pure torsion. In general, the constitutive models and relationships that characterize reinforced concrete are only as good as the experimental data that is gathered – ensuring that a rigorous and unbiased approach exists for calculating the Mohr’s circle of strain during an experiment, is of utmost importance to the structural research community.

Keywords: reinforced concrete, shell tests, Mohr’s circle, experimental research

Procedia PDF Downloads 235

Authors: M. Ndeley

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The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to no optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in South Africa and shipments of finished product to customers within the Country. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Keywords: production and transport scheduling problem, graph based scheduling, integrated scheduling

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Authors: Ali A. Abdollahi

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During the last several decades, scientists have consistently applied Multiple Criteria Decision-Making methods in making decisions about multi-faceted, complicated subjects. While making such decisions and in order to achieve more accurate evaluations, they have regularly used a variety of criteria instead of applying just one Optimum Evaluation Criterion. The method presented here utilizes both ‘quantity’ and ‘quality’ to assess the function of the Multiple-Criteria method. Applying Data envelopment analysis (DEA), weighted aggregated sum product assessment (WASPAS), Weighted Sum Approach (WSA), Analytic Network Process (ANP), and Charnes, Cooper, Rhodes (CCR) methods, we have analyzed thirteen parks in Kerman city. It further indicates that the functions of WASPAS and WSA are compatible with each other, but also that their deviation from DEA is extensive. Finally, the results for the CCR technique do not match the results of the DEA technique. Our study indicates that the ANP method, with the average rate of 1/51, ranks closest to the DEA method, which has an average rate of 1/49.

Keywords: multiple criteria decision making, Data envelopment analysis (DEA), Charnes Cooper Rhodes (CCR), Weighted Sum Approach (WSA)

Procedia PDF Downloads 218

Authors: Hadj Sahraoui Omar, Kebir Lahcen Wahib, Bennia Ahmed

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Interferometric Synthetic Aperture Radar (InSAR) coherence is a crucial parameter for accurately monitoring ground deformation and environmental changes. However, coherence can be degraded by various factors such as temporal decorrelation, atmospheric disturbances, and geometric misalignments, limiting the reliability of InSAR measurements (Omar Hadj‐Sahraoui and al. 2019). To address this challenge, we propose an innovative hybrid approach that combines artificial intelligence (AI) with advanced filtering techniques to optimize interferometric coherence in InSAR data. Specifically, we introduce a Convolutional Neural Network (CNN) integrated with the Lee filter to enhance the performance of radar interferometry. This hybrid method leverages the strength of CNNs to automatically identify and mitigate the primary sources of decorrelation, while the Lee filter effectively reduces speckle noise, improving the overall quality of interferograms. We develop a deep learning-based model trained on multi-temporal and multi-frequency SAR datasets, enabling it to predict coherence patterns and enhance low-coherence regions. This hybrid CNN-SAR with Lee filtering significantly reduces noise and phase unwrapping errors, leading to more precise deformation maps. Experimental results demonstrate that our approach improves coherence by up to 30% compared to traditional filtering techniques, making it a robust solution for challenging scenarios such as urban environments, vegetated areas, and rapidly changing landscapes. Our method has potential applications in geohazard monitoring, urban planning, and environmental studies, offering a new avenue for enhancing InSAR data reliability through AI-powered optimization combined with robust filtering techniques.

Keywords: CNN-SAR, Lee Filter, hybrid optimization, coherence, InSAR phase unwrapping, speckle noise reduction

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Authors: Zuoji Huang, Haiming Qian, Chunlin Wang, Jinyan Sun, Nan Xu

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In this paper, the strategy to extract double road edge lines from acquired road stripe image was explored. The workflow is as follows: the road stripes are acquired by probabilistic boosting tree algorithm and morphological algorithm immediately, and road centerlines are detected by thinning algorithm, so the initial road edge lines can be acquired along the road centerlines. Then we refine the results with big variation of local curvature of centerlines. Specifically, the energy function of edge line is constructed by gradient feature and spectral information, and Dijkstra algorithm is used to optimize the initial road edge lines. The Snake model is constructed to solve the fracture problem of intersection, and the discrete dynamic programming algorithm is used to solve the model. After that, we could get the final road network. Experiment results show that the strategy proposed in this paper can be used to extract the continuous and smooth road edge lines from high-resolution remote sensing images with an accuracy of 88% in our study area.

Keywords: road edge lines extraction, energy function, intersection fracture, Snake model

Procedia PDF Downloads 338

Authors: A. Baqqal, O. Abduhamid, H. Abdul-Hameed, T. Messager, G. Ayoub

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In this contribution, the effect of crystal content on the cyclic response of semi-crystalline polyethylene is studied over a large strain range. Experimental observations on a high-density polyethylene with 72% crystal content and an ultralow density polyethylene with 15% crystal content are reported. The cyclic stretching does appear a thermoplastic-like response for high crystallinity and an elastomeric-like response for low crystallinity, both characterized by a stress-softening, a hysteresis and a residual strain, whose amount depends on the crystallinity and the applied strain. Based on the experimental observations, a unified viscoelastic-viscoplastic constitutive model capturing the polyethylene cyclic response features is proposed. A two-phase representation of the polyethylene microstructure allows taking into consideration the effective contribution of the crystalline and amorphous phases to the intermolecular resistance to deformation which is coupled, to capture the strain hardening, to a resistance to molecular orientation. The polyethylene cyclic response features are captured by introducing evolution laws for the model parameters affected by the microstructure alteration due to the cyclic stretching.

Keywords: cyclic loading unloading, polyethylene, semi-crystalline polymer, viscoelastic-viscoplastic constitutive model

Procedia PDF Downloads 224

Authors: Nesreddine Djafar Henni, Akram Khelaifia, Salah Guettala, Rachid Chebili

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Reinforced concrete shear walls and vertical plate-like elements play a pivotal role in efficiently managing a building's response to seismic forces. This study investigates how the performance of reinforced concrete buildings equipped with shear walls featuring different shear wall-to-frame stiffness ratios aligns with the requirements stipulated in the Algerian seismic code RPA99v2003, particularly in high-seismicity regions. Seven distinct 3D finite element models are developed and evaluated through nonlinear static analysis. Engineering Demand Parameters (EDPs) such as lateral displacement, inter-story drift ratio, shear force, and bending moment along the building height are analyzed. The findings reveal two predominant categories of induced responses: force-based and displacement-based EDPs. Furthermore, as the shear wall-to-frame ratio increases, there is a concurrent increase in force-based EDPs and a decrease in displacement-based ones. Examining the distribution of shear walls from both force and displacement perspectives, model G with the highest stiffness ratio, concentrating stiffness at the building's center, intensifies induced forces. This configuration necessitates additional reinforcements, leading to a conservative design approach. Conversely, model C, with the lowest stiffness ratio, distributes stiffness towards the periphery, resulting in minimized induced shear forces and bending moments, representing an optimal scenario with maximal performance and minimal strength requirements.

Keywords: dual RC buildings, RC shear walls, modeling, static nonlinear pushover analysis, optimization, seismic performance

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Authors: Juan Li, Jiawen Qiu, Yuchuan Chu, Tianping Zhang, Wei Meng, Yanhui Jia, Xiaohui Liu

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This article developed an ion thruster optic system sputter erosion depth numerical 3D model by IFE-PIC (Immersed Finite Element-Particle-in-Cell) and Mont Carlo method, and calculated the downstream surface sputter erosion rate of accelerator grid; Compared with LIPS-200 life test data, the results of the numerical model are in reasonable agreement with the measured data. Finally, we predict the lifetime of the 20cm diameter ion thruster via the erosion data obtained with the model. The ultimate result demonstrates that under normal operating condition, the erosion rate of the grooves wears on the downstream surface of the accelerator grid is 34.6μm⁄1000h, which means the conservative lifetime until structural failure occurring on the accelerator grid is 11500 hours.

Keywords: ion thruster, accelerator gird, sputter erosion, lifetime assessment

Procedia PDF Downloads 565

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

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

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

Procedia PDF Downloads 188

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Han-Mei Chen, Rongxin Zhou, Taige Wang

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Glass is widely used in everyday life, from glass bottles for beverages to architectural glass for various forms of glazing. Although the mainstream of used glass is recycled in the UK, the single-use and then recycling procedure results in a lot of waste as it incorporates intact glass with smashing, re-melting, and remanufacturing. These processes bring massive energy consumption with a huge loss of high embodied energy and economic value, compared to re-use, which’s towards a ‘zero carbon’ target. As a tourism city, Liverpool has more glass bottle consumption than most less leisure-focused cities. It’s therefore vital for Liverpool to find an upgrading approach for the single-use glass bottles with low carbon output. This project aims to assess the feasibility of industrial symbiosis and upgrading the framework of glass and to investigate the ways of achieving them. It is significant to Liverpool’s future industrial strategy since it provides an opportunity to target economic recovery for post-COVID by industry symbiosis and up-grading waste management in Liverpool to respond to the climate emergency. In addition, it will influence the local government policy for glass bottle reuse and recycling in North West England and as a good practice to be further recommended to other areas of the UK. First, a critical literature review of glass waste strategies has been conducted in the UK and worldwide industrial symbiosis practices. Second, mapping, data collection, and analysis have shown the current life cycle chain and the strong links of glass reuse and upgrading potentials via site visits to 16 local waste recycling centres. The results of this research have demonstrated the understanding of the influence of key factors on the development of a circular industrial symbiosis business model for beverage glass bottles. The current waste management procedures of the glass bottle industry, its business model, supply chain, and material flow have been reviewed. The various potential opportunities for glass bottle up-valuing have been investigated towards an industrial symbiosis in Liverpool. Finally, an up-valuing business model has been developed for an industrial symbiosis framework of glass in Liverpool. For glass bottles, there are two possibilities 1) focus on upgrading processes towards re-use rather than single-use and recycling and 2) focus on ‘smart’ re-use and recycling, leading to optimised values in other sectors to create a wider industry symbiosis for a multi-level and circular economy.

Keywords: glass bottles, industry symbiosis, smart re-use, waste upgrading

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Authors: Sabrina Neururer, Marco Schweitzer, Werner Hackl, Bernhard Tilg, Patrick Raudaschl, Andreas Huber, Bernhard Pfeifer

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History and the current outbreak of Covid-19 have shown the deadly potential of infectious diseases. However, infectious diseases also have a serious impact on areas other than health and healthcare, such as the economy or social life. These areas are strongly codependent. Therefore, disease control measures, such as social distancing, quarantines, curfews, or lockdowns, have to be adopted in a very considerate manner. Infectious disease modeling can support policy and decision-makers with adequate information regarding the dynamics of the pandemic and therefore assist in planning and enforcing appropriate measures that will prevent the healthcare system from collapsing. In this work, an agent-based simulation package named “survival” for simulating infectious diseases is presented. A special focus is put on SARS-Cov-2. The presented simulation package was used in Austria to model the SARS-Cov-2 outbreak from the beginning of 2020. Agent-based modeling is a relatively recent modeling approach. Since our world is getting more and more complex, the complexity of the underlying systems is also increasing. The development of tools and frameworks and increasing computational power advance the application of agent-based models. For parametrizing the presented model, different data sources, such as known infections, wastewater virus load, blood donor antibodies, circulating virus variants and the used capacity for hospitalization, as well as the availability of medical materials like ventilators, were integrated with a database system and used. The simulation result of the model was used for predicting the dynamics and the possible outcomes and was used by the health authorities to decide on the measures to be taken in order to control the pandemic situation. The survival package was implemented in the programming language Java and the analytics were performed with R Studio. During the first run in March 2020, the simulation showed that without measures other than individual personal behavior and appropriate medication, the death toll would have been about 27 million people worldwide within the first year. The model predicted the hospitalization rates (standard and intensive care) for Tyrol and South Tyrol with an accuracy of about 1.5% average error. They were calculated to provide 10-days forecasts. The state government and the hospitals were provided with the 10-days models to support their decision-making. This ensured that standard care was maintained for as long as possible without restrictions. Furthermore, various measures were estimated and thereafter enforced. Among other things, communities were quarantined based on the calculations while, in accordance with the calculations, the curfews for the entire population were reduced. With this framework, which is used in the national crisis team of the Austrian province of Tyrol, a very accurate model could be created on the federal state level as well as on the district and municipal level, which was able to provide decision-makers with a solid information basis. This framework can be transferred to various infectious diseases and thus can be used as a basis for future monitoring.

Keywords: modelling, simulation, agent-based, SARS-Cov-2, COVID-19

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Authors: Lucia Perez, Maria Gaton y Santa Palella

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Starting from the etymology of the word culture, the Latin term ‘colere’, whose meaning is to cultivate, we understand that the society that cultivates its knowledge is laying the foundations for new possibilities. In this sense, art and fashion contain the same attributes: concept, aesthetic principles, and refined techniques. Both play a crucial role, communication, and this implies a sense of community, relationship with tradition, and innovation. This is the mirror in which to contemplate, but also the space that helps to grow. This is the framework where our object of study opens up: the anthropological management or the mission management model applied to fashion exhibitions in museums and cultural institutions. For this purpose, a bibliographic review has been carried out with its subsequent analysis, a case study of three successful exhibitions: ‘Christian Dior: designer of dreams’, ‘Balenciaga and the Spanish painting’, and ‘China: Through the Looking Glass’. The methodology has been completed with interviews focused on the curators. Amongst the results obtained, it is worth highlighting the fundamental role of transcendent leadership, which, in addition to being results-oriented, must align the motivations of the collaborators with the mission. The anthropological management model conceives management as a service, and it is oriented to the interests of the staff and the public, in short, of the person; this is what enables the objectives of effectiveness, efficiency, and social value to be achieved; dimensions, all necessary for the proper development of the mission of the exhibitions. Fashion, understood as art, is at the service of culture, and therefore of the human being, which defines a transcendent mission. We conclude that the profile of an anthropological management model applied to fashion exhibitions in museums is the ideal one to achieve the purpose of these institutions.

Keywords: art, culture, fashion, anthropological model, fashion exhibitions

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Authors: David Rafetseder, Walter Bauer, Florian Poltschak, Wolfgang Amrhein

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A flat double-stator linear induction machine (DSLIM) with a short squirrel-cage mover is designed for high thrust force at moderate speed < 5m/s. The performance and motor parameters are determined on the basis of a 2D time-transient simulation with the finite element (FE) software Maxwell 2015. Design guidelines and transformation rules for space vector theory of the LIM are presented. Resulting thrust calculated by flux and current vectors is compared with the FE results showing good coherence and reduced noise. The parameters of the equivalent circuit model are obtained.

Keywords: equivalent circuit model, finite element model, linear induction motor, space vector theory

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Authors: Tayyaba Azim, Bibi Amina

Abstract:

The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Kushal Kumar Chode, Remus Miahi Cirstea

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In recent times, the drive towards more efficient vehicles and the increase in the number of vehicle on the roads has driven the aerodynamic researchers from studying the vehicle in isolation towards understanding the benefits of vehicle platooning. Vehicle platooning is defined as a series of vehicles traveling in close proximity. Due to the limitations in size and load measurement capabilities for the wind tunnels facilities, it is very difficult to perform this investigation experimentally. In this paper, the use of chimera or overset meshing technique is used within the STARCCM+ software to model the flow surrounding two identical vehicle models travelling in close proximity and also during an overtaking maneuver. The results are compared with data obtained from a polyhedral mesh and identical physics conditions. The benefits in terms of computational time and resources and the accuracy of the overset mesh approach are investigated.

Keywords: chimera mesh, computational accuracy, overset mesh, platooning vehicles

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Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

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Authors: M. Sumida, S. Morita

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In this research, the effect of unsteady flows acting on road vehicles was experimentally investigated, using an advanced and recently introduced wind tunnel. The aims of this study were to extract the characteristics of fluid forces acting on road vehicles under unsteady wind conditions and obtain new information on drag forces in a practical on-road test. We applied pulsating wind as a representative example of the atmospheric fluctuations that vehicles encounter on the road. That is, we considered the case where the vehicles are moving at constant speed in the air, with large wind oscillations. The experimental tests were performed on the Ahmed-type test model, which is a simplified vehicle model. This model was chosen because of its simplicity and the data accumulated under steady wind conditions. The experiments were carried out with a time-averaged Reynolds number of Re = 4.16x10⁵ and a pulsation period of T = 1.5 s, with amplitude of η = 0.235. Unsteady fluid forces of drag and lift were obtained utilizing a multi-component load cell. It was observed that the unsteady aerodynamic forces differ significantly from those under steady wind conditions. They exhibit a phase shift and an enhanced response to the wind oscillations. Furthermore, their behavior depends on the slant angle of the rear shape of the model.

Keywords: Ahmed body, automotive aerodynamics, unsteady wind, wind tunnel test

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Authors: Khaled Yahia

Abstract:

This paper deals with the problem of stator faults diagnosis in induction motors. Using the discrete wavelet transform (DWT) for the current Park’s vector modulus (CPVM) analysis, the inter-turn short-circuit faults diagnosis can be achieved. This method is based on the decomposition of the CPVM signal, where wavelet approximation and detail coefficients of this signal have been extracted. The energy evaluation of a known bandwidth detail permits to define a fault severity factor (FSF). This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. Simulation, as well as experimental, results show the effectiveness of the used method.

Keywords: induction motors (IMs), inter-turn short-circuits diagnosis, discrete wavelet transform (DWT), current park’s vector modulus (CPVM)

Procedia PDF Downloads 569