Search results for: geometric search algorithm

Authors: Ashkan Golgoon, Souhayl Sadik, Arash Yavari

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Eigenstrains in nonlinear solids are created due to anelastic effects such as non-uniform temperature distributions, growth, remodeling, and defects. Eigenstrains understanding is indispensable, as they can generate residual stresses and strongly affect the overall response of solids. Here, we study the residual stress and deformation fields of an incompressible isotropic infinite wedge with a circumferentially-symmetric distribution of finite eigenstrains. We construct a material manifold, whose Riemannian metric explicitly depends on the eigenstrain distribution, thereby we turn the problem into a classical nonlinear elasticity problem, where we find an embedding of the Riemannian material manifold into the ambient Euclidean space. In particular, we find exact solutions for the residual stress and deformation fields of a neo-Hookean wedge having a symmetric inclusion with finite radial and circumferential eigenstrains. Moreover, we numerically solve a similar problem when a symmetric Mooney-Rivlin inhomogeneity with finite eigenstrains is placed in a neo-Hookean wedge. Generalization of the eigenstrain problem to other geometries are also discussed.

Keywords: finite eigenstrains, geometric mechanics, inclusion, inhomogeneity, nonlinear elasticity

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Authors: Vitalii Yu. Kapitan, Aleksandr V. Perzhu, Konstantin V. Nefedev

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The necessity of studying magnetic multilayer structures is explained by the prospects of their practical application as a technological base for creating new storages medium. Magnetic multilayer films have many unique features that contribute to increasing the density of information recording and the speed of storage devices. Multilayer structures are structures of alternating magnetic and nonmagnetic layers. In frame of the classical Heisenberg model, lattice spin systems with direct short- and long-range exchange interactions were investigated by Monte Carlo methods. The thermodynamic characteristics of multilayer structures, such as the temperature behavior of magnetization, energy, and heat capacity, were investigated. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. The developed software is based on the new, promising programming language Rust. Rust is a new experimental programming language developed by Mozilla. The language is positioned as an alternative to C and C++. For the Monte Carlo simulation, the Metropolis algorithm and its parallel implementation using MPI and the Wang-Landau algorithm were used. We are planning to study of magnetic multilayer films with asymmetric Dzyaloshinskii–Moriya (DM) interaction, interfacing effects and skyrmions textures. This work was supported by the state task of the Ministry of Education and Science of the Russia # 3.7383.2017/8.9

Keywords: The Monte Carlo methods, Heisenberg model, multilayer structures, magnetic skyrmion

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Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

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Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

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Authors: Christine Böckmann, Julia Rosemann

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We present an inversion algorithm that is used in the European Aerosol Lidar Network for the inversion of data collected with multi-wavelength Raman lidar. These instruments measure backscatter coefficients at 355, 532, and 1064 nm, and extinction coefficients at 355 and 532 nm. The algorithm is based on manually controlled inversion of optical data which allows for detailed sensitivity studies and thus provides us with comparably high quality of the derived data products. The algorithm allows us to derive particle effective radius, volume, surface-area concentration with comparably high confidence. The retrieval of the real and imaginary parts of the complex refractive index still is a challenge in view of the accuracy required for these parameters in climate change studies in which light-absorption needs to be known with high accuracy. Single-scattering albedo (SSA) can be computed from the retrieve microphysical parameters and allows us to categorize aerosols into high and low absorbing aerosols. From mathematical point of view the algorithm is based on the concept of using truncated singular value decomposition as regularization method. This method was adapted to work for the retrieval of the particle size distribution function (PSD) and is called hybrid regularization technique since it is using a triple of regularization parameters. The inversion of an ill-posed problem, such as the retrieval of the PSD, is always a challenging task because very small measurement errors will be amplified most often hugely during the solution process unless an appropriate regularization method is used. Even using a regularization method is difficult since appropriate regularization parameters have to be determined. Therefore, in a next stage of our work we decided to use two regularization techniques in parallel for comparison purpose. The second method is an iterative regularization method based on Pade iteration. Here, the number of iteration steps serves as the regularization parameter. We successfully developed a semi-automated software for spherical particles which is able to run even on a parallel processor machine. From a mathematical point of view, it is also very important (as selection criteria for an appropriate regularization method) to investigate the degree of ill-posedness of the problem which we found is a moderate ill-posedness. We computed the optical data from mono-modal logarithmic PSD and investigated particles of spherical shape in our simulations. We considered particle radii as large as 6 nm which does not only cover the size range of particles in the fine-mode fraction of naturally occurring PSD but also covers a part of the coarse-mode fraction of PSD. We considered errors of 15% in the simulation studies. For the SSA, 100% of all cases achieve relative errors below 12%. In more detail, 87% of all cases for 355 nm and 88% of all cases for 532 nm are well below 6%. With respect to the absolute error for non- and weak-absorbing particles with real parts 1.5 and 1.6 in all modes the accuracy limit +/- 0.03 is achieved. In sum, 70% of all cases stay below +/-0.03 which is sufficient for climate change studies.

Keywords: aerosol particles, inverse problem, microphysical particle properties, regularization

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Authors: C. A. Anser Pasha

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In this paper, a new Vehicular Speed Detection Camera System that is applicable as an alternative to traditional radars with the same accuracy or even better is presented. The real-time measurement and analysis of various traffic parameters such as speed and number of vehicles are increasingly required in traffic control and management. Image processing techniques are now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on image processing techniques have been applied to detect multiple vehicles and track them. The SDCS processes can be divided into three successive phases; the first phase is Objects detection phase, which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction. The second phase is Objects tracking, which consists of three successive operations - object segmentation, object labeling, and object center extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like simple tracking, the object has left the scene, the object has entered the scene, object crossed by another object, and object leaves and another one enters the scene. The third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass by the scene.

Keywords: radar, image processing, detection, tracking, segmentation

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Authors: Lin Huo, Xianwei Liu, Jingxiong Zhou

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Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective.

Keywords: image auto-annotation, color correlograms, Hash code, image retrieval

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Authors: Andrea Tundis, Carlos García Cordero, Rolf Egert, Alfredo Garro, Max Mühlhäuser

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Resilience is an important system property that relies on the ability of a system to automatically recover from a degraded state so as to continue providing its services. Resilient systems have the means of detecting faults and failures with the added capability of automatically restoring their normal operations. Mastering resilience in the domain of Cyber-Physical Systems is challenging due to the interdependence of hybrid hardware and software components, along with physical limitations, laws, regulations and standards, among others. In order to overcome these challenges, this paper presents a modeling approach, based on the concept of Dynamic Cells, tailored to the management of Smart Grids. Additionally, a heuristic algorithm that works on top of the proposed modeling approach, to find resilient configurations, has been defined and implemented. More specifically, the model supports a flexible representation of Smart Grids and the algorithm is able to manage, at different abstraction levels, the resource consumption of individual grid elements on the presence of failures and faults. Finally, the proposal is evaluated in a test scenario where the effectiveness of such approach, when dealing with complex scenarios where adequate solutions are difficult to find, is shown.

Keywords: cyber-physical systems, energy management, optimization, smart grids, self-healing, resilience, security

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Authors: Michael Dreyfuss, Yahel Giat

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Limited battery range and long recharging times are the greatest obstacles to the successful adoption of electric cars. One of the suggestions to overcome these problems is that carmakers retain ownership of batteries and provide battery swapping service so that customers exchange their depleted batteries for recharged batteries. Motivated by this example, we consider the problem of optimal spares allocation in an exchangeable-item, multi-location repair system. We generalize the standard service measures of fill rate and average waiting time to reflect the fact that customers penalize the service provider only if they have to wait more than a ‘tolerable’ time window. These measures are denoted as the window fill rate and the truncated waiting time, respectively. We find that the truncated waiting time is convex and therefore a greedy algorithm solves the spares allocation problem efficiently. We show that the window fill rate is generally S-shaped and describe an efficient algorithm to find a near-optimal solution and detail a priori and a posteriori upper bounds to the distance from optimum. The theory is complemented with a large scale numerical example demonstrating the spare battery allocation in battery swapping stations.

Keywords: convex-concave optimization, exchangeable item, M/G/infinity, multiple location, repair system, spares allocation, window fill rate

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: M. Yushalify Misro, Ahmad Ramli, Jamaludin M. Ali

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Bezier curves have useful properties for path generation problem, for instance, it can generate the reference trajectory for vehicles to satisfy the path constraints. Both algorithms join cubic Bezier curve segment smoothly to generate the path. Some of the useful properties of Bezier are curvature. In mathematics, the curvature is the amount by which a geometric object deviates from being flat, or straight in the case of a line. Another extrinsic example of curvature is a circle, where the curvature is equal to the reciprocal of its radius at any point on the circle. The smaller the radius, the higher the curvature thus the vehicle needs to bend sharply. In this study, we use Bezier curve to fit highway-like curve. We use the different approach to finding the best approximation for the curve so that it will resemble highway-like curve. We compute curvature value by analytical differentiation of the Bezier Curve. We will then compute the maximum speed for driving using the curvature information obtained. Our research works on some assumptions; first the Bezier curve estimates the real shape of the curve which can be verified visually. Even, though, the fitting process of Bezier curve does not interpolate exactly on the curve of interest, we believe that the estimation of speed is acceptable. We verified our result with the manual calculation of the curvature from the map.

Keywords: speed estimation, path constraints, reference trajectory, Bezier curve

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Authors: Sylwia Sumińska, Łukasz Kapica, Grzegorz Szczepański

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Introduction: Cognitive skills are a crucial part of everyday functioning. Cognitive skills include perception, attention, language, memory, executive functions, and higher cognitive skills. With the aging of societies, there is an increasing percentage of people whose cognitive skills decline. Cognitive skills affect work performance. The appropriate diagnosis of a worker’s cognitive skills reduces the risk of errors and accidents at work which is also important for senior workers. The study aimed to prepare new cognitive tests for adults aged 20-60 and assess the psychometric properties of the tests. The project responds to the need for reliable and accurate methods of assessing cognitive performance. Computer tests were developed to assess psychomotor performance, attention, and working memory. Method: Two hundred eighty people aged 20-60 will participate in the study in 4 age groups. Inclusion criteria for the study were: no subjective cognitive impairment, no history of severe head injuries, chronic diseases, psychiatric and neurological diseases. The research will be conducted from February - to June 2022. Cognitive tests: 1) Measurement of psychomotor performance: Reaction time, Reaction time with selective attention component; 2) Measurement of sustained attention: Visual search (dots), Visual search (numbers); 3) Measurement of working memory: Remembering words, Remembering letters. To assess the validity and the reliability subjects will perform the Vienna Test System, i.e., “Reaction Test” (reaction time), “Signal Detection” (sustained attention), “Corsi Block-Tapping Test” (working memory), and Perception and Attention Test (TUS), Colour Trails Test (CTT), Digit Span – subtest from The Wechsler Adult Intelligence Scale. Eighty people will be invited to a session after three months aimed to assess the consistency over time. Results: Due to ongoing research, the detailed results from 280 people will be shown at the conference separately in each age group. The results of correlation analysis with the Vienna Test System will be demonstrated as well.

Keywords: aging, attention, cognitive skills, cognitive tests, psychomotor performance, working memory

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Abdelaziz Beddiaf

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In this work, we present an analysis based on the study of mobility which is a very important electrical parameter of a piezoresistor and which is directly bound to the piezoresistivity effect in piezoresistive pressure sensors. We determine how the temperature affects mobility when the electric potential is applied. For this, a theoretical approach based on mobility in a p-type Silicon piezoresistor with that of a finite difference model for self-heating is developed. So, the evolution of mobility has been established versus time for different doping levels and with temperature rise provoked by self-heating using a numerical model combined with that of mobility. Furthermore, it has been calculated for some geometrical parameters of the sensor, such as membrane side length and thickness. Also, it is computed as a function of bias voltage. It was observed that mobility is strongly affected by the temperature rise induced by the applied potential when the sensor is actuated for a prolonged time as a consequence of drifting in the output response of the sensor. Finally, this work makes it possible to predict their temperature behavior due to self-heating and to improve this effect by optimizing the geometric properties of the device and by reducing the voltage source applied to the bridge.

Keywords: Sensors, Piezoresistivity, Mobility, Bias voltage

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Authors: Sara Zelman, Courtney Meyer, Hiren Patel, Lisa Philpotts, Sue Lahey, Thomas Burke

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Background: As the global burden of disease shifts from infectious diseases to noncommunicable diseases, there is growing urgency to provide treatment for time-sensitive illnesses, such as ST-Elevation Myocardial Infarctions (STEMIs). The standard of care for STEMIs in developed countries is Percutaneous Coronary Intervention (PCI). However, this is inaccessible in resource-limited settings. Before the discovery of PCI, Streptokinase (STK) and other thrombolytic drugs were first-line treatments for STEMIs. STK has been recognized as a cost-effective and safe treatment for STEMIs; however, in settings which lack access to PCI, it has not become the established second-line therapy. A systematic literature review was conducted to geographically map the use of STK for STEMIs in resource-limited settings. Methods: Our literature review group searched the databases Cinhal, Embase, Ovid, Pubmed, Web of Science, and WHO’s Index Medicus. The search terms included ‘thrombolytics’ AND ‘myocardial infarction’ AND ‘resource-limited’ and were restricted to human studies and papers written in English. A considerable number of studies came from Latin America; however, these studies were not written in English and were excluded. The initial search yielded 3,487 articles, which was reduced to 3,196 papers after titles were screened. Three medical professionals then screened abstracts, from which 291 articles were selected for full-text review and 94 papers were chosen for final inclusion. These articles were then analyzed and mapped geographically. Results: This systematic literature review revealed that STK has been used for the treatment of STEMIs in 33 resource-limited countries, with 18 of 94 studies taking place in India. Furthermore, 13 studies occurred in Pakistan, followed by Iran (6), Sri Lanka (5), Brazil (4), China (4), and South Africa (4). Conclusion: Our systematic review revealed that STK has been used for the treatment of STEMIs in 33 resource-limited countries, with the highest utilization occurring in India. This demonstrates that even though STK has high utility for STEMI treatment in resource-limited settings, it still has not become the standard of care. Future research should investigate the barriers preventing the establishment of STK use as second-line treatment after PCI.

Keywords: cardiovascular disease, global health, resource-limited setting, ST-Elevation Myocardial Infarction, Streptokinase

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Tan Zhang, Zhongjian Wang, Jack Xin, Zhiwen Zhang

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We aim to efficiently compute the spreading speeds of reaction-diffusion-advection (RDA) fronts in divergence-free random flows under the Kolmogorov-Petrovsky-Piskunov (KPP) nonlinearity. We study a stochastic interacting particle method (IPM) for the reduced principal eigenvalue (Lyapunov exponent) problem of an associated linear advection-diffusion operator with spatially random coefficients. The Fourier representation of the random advection field and the Feynman-Kac (FK) formula of the principal eigenvalue (Lyapunov exponent) form the foundation of our method implemented as a genetic evolution algorithm. The particles undergo advection-diffusion and mutation/selection through a fitness function originated in the FK semigroup. We analyze the convergence of the algorithm based on operator splitting and present numerical results on representative flows such as 2D cellular flow and 3D Arnold-Beltrami-Childress (ABC) flow under random perturbations. The 2D examples serve as a consistency check with semi-Lagrangian computation. The 3D results demonstrate that IPM, being mesh-free and self-adaptive, is simple to implement and efficient for computing front spreading speeds in the advection-dominated regime for high-dimensional random flows on unbounded domains where no truncation is needed.

Keywords: KPP front speeds, random flows, Feynman-Kac semigroups, interacting particle method, convergence analysis

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Authors: Nurulhikma Md Isa, Intan Elya Suka, Nur Farhana Roslan, Chew Bee Lynn

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The evolutionarily conserved N-end rule pathway marks proteins for degradation by the Ubiquitin Proteosome System (UPS) based on the nature of their N-terminal residue. Proteins with a destabilizing N-terminal residue undergo a series of condition-dependent N-terminal modifications, resulting in their ubiquitination and degradation. Intensive research has been carried out in Arabidopsis previously. The group VII Ethylene Response Factor (ERFs) transcription factors are the first N-end rule pathway substrates found in Arabidopsis and their role in regulating oxygen sensing. ERFs also function as central hubs for the perception of gaseous signals in plants and control different plant developmental including germination, stomatal aperture, hypocotyl elongation and stress responses. However, nothing is known about the role of this pathway during fruit development and ripening aspect. The plant model system Arabidopsis cannot represent fleshy fruit model system therefore tomato is the best model plant to study. PROTEOLYSIS6 (PRT6) is an E3 ubiquitin ligase of the N-end rule pathway. Two homologs of PRT6 sequences have been identified in tomato genome database using the PRT6 protein sequence from model plant Arabidopsis thaliana. Homology search against Ensemble Plant database (tomato) showed Solyc09g010830.2 is the best hit with highest score of 1143, e-value of 0.0 and 61.3% identity compare to the second hit Solyc10g084760.1. Further homology search was done using NCBI Blast database to validate the data. The result showed best gene hit was XP_010325853.1 of uncharacterized protein LOC101255129 (Solanum lycopersicum) with highest score of 1601, e-value 0.0 and 48% identity. Both Solyc09g010830.2 and uncharacterized protein LOC101255129 were genes located at chromosome 9. Further validation was carried out using BLASTP program between these two sequences (Solyc09g010830.2 and uncharacterized protein LOC101255129) to investigate whether they were the same proteins represent PRT6 in tomato. Results showed that both proteins have 100 % identity, indicates that they were the same gene represents PRT6 in tomato. In addition, we used two different RNAi constructs that were driven under 35S and Polygalacturonase (PG) promoters to study the function of PRT6 during tomato developmental stages and ripening processes.

Keywords: ERFs, PRT6, tomato, ubiquitin

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Authors: Elham Allahmoradi, Bahman Allahmoradi, Ali Bonyadi Naeini

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Complex systems have many aspects. A variety of methods have been developed to analyze these systems. The most efficient of these methods should not only be simple, but also provide useful and comprehensive information about many aspects of the system. Matrix methods are considered the most commonly methods used to analyze and design systems. Each matrix method can examine a particular aspect of the system. If these methods are combined, managers can access to more comprehensive and broader information about the system. This study was conducted in four steps. In the first step, a process model of a real project has been extracted through IDEF3. In the second step, activity levels have been attained by writing a process model in the form of a design structure matrix (DSM) and sorting it through triangulation algorithm (TA). In the third step, sub-processes have been obtained by writing the process model in the form of an interface structure matrix (ISM) and clustering it through cluster identification algorithm (CIA). In the fourth step, a mixed model has been developed to provide a unified picture of the project structure through the simultaneous presentation of activities and sub-processes. Finally, the paper is completed with a conclusion.

Keywords: integrated definition for process description capture (IDEF3) method, design structure matrix (DSM), interface structure matrix (ism), mixed matrix model, activity level, sub-process

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Authors: Suguru Miyauchi, Toshiyuki Hayase

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Biological membranes have selective permeability, and the capsules or cells enclosed by the membrane show the deformation by the osmotic flow. This mass transport phenomenon is observed everywhere in a living body. For the understanding of the mass transfer in a body, it is necessary to consider the mass transfer phenomenon across the membrane as well as the deformation of the membrane by a flow. To our knowledge, in the numerical analysis, the method for mass transfer across the moving membrane has not been established due to the difficulty of the treating of the mass flux permeating through the moving membrane with selective permeability. In the existing methods for the mass transfer across the membrane, the approximate delta function is used to communicate the quantities on the interface. The methods can reproduce the permeation of the solute, but cannot reproduce the non-permeation. Moreover, the computational accuracy decreases with decreasing of the permeable coefficient of the membrane. This study aims to develop the numerical method capable of treating three-dimensional problems of mass transfer across the moving flexible membrane. One of the authors developed the numerical method with high accuracy based on the finite element method. This method can capture the discontinuity on the membrane sharply due to the consideration of the jumps in concentration and concentration gradient in the finite element discretization. The formulation of the method takes into account the membrane movement, and both permeable and non-permeable membranes can be treated. However, searching the cross points of the membrane and fluid element boundaries and splitting the fluid element into sub-elements are needed for the numerical integral. Therefore, cumbersome operation is required for a three-dimensional problem. In this paper, we proposed an improved method to avoid the search and split operations, and confirmed its effectiveness. The membrane shape was treated implicitly by introducing the level set function. As the construction of the level set function, the membrane shape in one fluid element was expressed by the shape function of the finite element method. By the numerical experiment, it was found that the shape function with third order appropriately reproduces the membrane shapes. The same level of accuracy compared with the previous method using search and split operations was achieved by using a number of sampling points of the numerical integral. The effectiveness of the method was confirmed by solving several model problems.

Keywords: finite element method, level set method, mass transfer, membrane permeability

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Authors: Marcela de Oliveira, Guilherme Giacomini, Allan Felipe Fattori Alves, Ana Luiza Menegatti Pavan, Maria Eugenia Dela Rosa, Fernando Antonio Bacchim Neto, Diana Rodrigues de Pina

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It is estimated that each year one death every 10 seconds (about 2 million deaths) in the world is attributed to tuberculosis (TB). Even after effective treatment, TB leaves sequelae such as, for example, pulmonary fibrosis, compromising the quality of life of patients. Evaluations of the aforementioned sequel are usually performed subjectively by radiology specialists. Subjective evaluation may indicate variations inter and intra observers. The examination of x-rays is the diagnostic imaging method most accomplished in the monitoring of patients diagnosed with TB and of least cost to the institution. The application of computational algorithms is of utmost importance to make a more objective quantification of pulmonary impairment in individuals with tuberculosis. The purpose of this research is the use of computer algorithms to quantify the pulmonary impairment pre and post-treatment of patients with pulmonary TB. The x-ray images of 10 patients with TB diagnosis confirmed by examination of sputum smears were studied. Initially the segmentation of the total lung area was performed (posteroanterior and lateral views) then targeted to the compromised region by pulmonary sequel. Through morphological operators and the application of signal noise tool, it was possible to determine the compromised lung volume. The largest difference found pre- and post-treatment was 85.85% and the smallest was 54.08%.

Keywords: algorithm, radiology, tuberculosis, x-rays exam

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

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This paper aims at developing a multi-objective model for supplier selection and order allocation problem in stochastic environment, where purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. In this regard, dependent chance programming is used which maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. The abovementioned stochastic multi-objective programming problem is then transformed into a stochastic single objective programming problem using minimum deviation method. In the next step, the further problem is solved applying a genetic algorithm, which performs a simulation process in order to calculate the stochastic objective function as its fitness function. Finally, the impact of stochastic parameters on the given solution is examined via a sensitivity analysis exploiting coefficient of variation. The results show that whatever stochastic parameters have greater coefficients of variation, the value of the objective function in the stochastic single objective programming problem is deteriorated.

Keywords: supplier selection, order allocation, dependent chance programming, genetic algorithm

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Authors: Rasikh Tariq, Fatima Z. Benarab

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Evaporative coolers has a minimum potential to reach the wet-bulb temperature of intake air which is not enough to handle a large cooling load; therefore, it is not a feasible option to overcome cooling requirement of a building. The invention of Maisotsenko (M) cycle has led evaporative cooling technology to reach the sub-wet-bulb temperature of the intake air; therefore, it brings an innovation in evaporative cooling techniques. In this work, we developed a mathematical model of the Maisotsenko based air cooler by applying energy and mass balance laws on different air channels. The governing ordinary differential equations are discretized and simulated on MATLAB. The temperature and the humidity plots are shown in the simulation results. A parametric study is conducted by varying working air inlet conditions (temperature and humidity), inlet air velocity, geometric parameters and water temperature. The influence of these aforementioned parameters on the cooling effectiveness of the HMX is reported.  Results have shown that the effectiveness of the M-Cycle is increased by increasing the ambient temperature and decreasing absolute humidity. An air velocity of 0.5 m/sec and a channel height of 6-8mm is recommended.

Keywords: HMX, maisotsenko cycle, mathematical modeling, numerical simulation, parametric study

Procedia PDF Downloads 142

Authors: Bastien Batardière, Joon Kwon

Abstract:

For finite-sum optimization, variance-reduced gradient methods (VR) compute at each iteration the gradient of a single function (or of a mini-batch), and yet achieve faster convergence than SGD thanks to a carefully crafted lower-variance stochastic gradient estimator that reuses past gradients. Another important line of research of the past decade in continuous optimization is the adaptive algorithms such as AdaGrad, that dynamically adjust the (possibly coordinate-wise) learning rate to past gradients and thereby adapt to the geometry of the objective function. Variants such as RMSprop and Adam demonstrate outstanding practical performance that have contributed to the success of deep learning. In this work, we present AdaLVR, which combines the AdaGrad algorithm with loopless variance-reduced gradient estimators such as SAGA or L-SVRG that benefits from a straightforward construction and a streamlined analysis. We assess that AdaLVR inherits both good convergence properties from VR methods and the adaptive nature of AdaGrad: in the case of L-smooth convex functions we establish a gradient complexity of O(n + (L + √ nL)/ε) without prior knowledge of L. Numerical experiments demonstrate the superiority of AdaLVR over state-of-the-art methods. Moreover, we empirically show that the RMSprop and Adam algorithm combined with variance-reduced gradients estimators achieve even faster convergence.

Keywords: convex optimization, variance reduction, adaptive algorithms, loopless

Procedia PDF Downloads 64

Authors: M. Saidi, F. Ait Medjber, B. Safi, M. Samar

Abstract:

This work is focused on the study of valuation of recycled concrete aggregates, by measuring certain properties of concrete in the fresh and hardened state. In this study, rheological tests and physic-mechanical characterization on concretes and mortars were conducted with recycled concrete whose geometric properties were identified aggregates. Mortars were elaborated with recycled fine aggregate (0/5mm) and concretes were manufactured using recycled coarse aggregates (5/12.5 mm and 12.5/20 mm). First, a study of the mortars was conducted to determine the effectiveness of adjuvant polycarboxylate superplasticizer on the workability of these and their action deflocculating of the fine recycled sand. The rheological behavior of mortars based on fine aggregate recycled was characterized. The results confirm that the mortars composed of different fractions of recycled sand (0/5) have a better mechanical properties (compressive and flexural strength) compared to normal mortar. Also, the mechanical strengths of concretes made with recycled aggregates (5/12.5 mm and 12.5/20 mm), are comparable to those of conventional concrete with conventional aggregates, provided that the implementation can be improved by the addition of a superplasticizer.

Keywords: demolition wastes, recycled coarse aggregate, concrete, workability, mechanical strength, porosity/water absorption

Procedia PDF Downloads 327

Authors: Khaled Ayfi, Morgan Dal, Frederic Coste, Nicolas Gallienne, Martina Ridlova, Philippe Lorong

Abstract:

In the gas industry, contamination of equipment by metal particles is one of the feared phenomena. Indeed, particles inside equipment can be driven by the gas flow and accumulate in places where the velocity is low. As they constitute a potential ignition hazard, particular attention is paid to the presence of particles in the oxygen industry. Indeed, the heat release from ignited particles may damage the equipment and even result in a loss of integrity. The objective of this work is to support the development of new design criteria. Studying the thermomechanical behavior of this equipment, thanks to numerical simulations, allows us to test the influence of various operating parameters (oxygen pressure, wall thickness, initial operating temperature, nature of the metal, etc.). Therefore, in this study, we propose a numerical model that describes the thermomechanical behavior of various pressurized installations heated locally by the combustion of small particles. This model takes into account the geometric and material nonlinearity and has been validated by the comparison of simulation results with experimental measurements obtained by a new device developed in this work.

Keywords: ignition, oxygen, numerical simulation, thermomechanical behaviour

Procedia PDF Downloads 147

Authors: John K. Avor, Choong-Koo Chang

Abstract:

The transfer of electrical loads at power generation stations from Standby Auxiliary Transformer (SAT) to Unit Auxiliary Transformer (UAT) and vice versa is through a fast bus transfer scheme. Fast bus transfer is a time-critical application where the transfer process depends on various parameters, thus transfer schemes apply advance algorithms to ensure power supply reliability and continuity. In a nuclear power generation station, supply continuity is essential, especially for critical class 1E electrical loads. Bus transfers must, therefore, be executed accurately within 4 to 10 cycles in order to achieve safety system requirements. However, the main problem is that there are instances where transfer schemes scrambled due to inaccurate interpretation of key parameters; and consequently, have failed to transfer several critical loads from UAT to the SAT during main generator trip event. Although several techniques have been adopted to develop robust transfer schemes, a combination of Artificial Neural Network and Fuzzy Systems (Neuro-Fuzzy) has not been extensively used. In this paper, we apply the concept of Neuro-Fuzzy to determine plant operating mode and dynamic prediction of the appropriate bus transfer algorithm to be selected based on the first cycle of voltage information. The performance of Sequential Fast Transfer and Residual Bus Transfer schemes was evaluated through simulation and integration of the Neuro-Fuzzy system. The objective for adopting Neuro-Fuzzy approach in the bus transfer scheme is to utilize the signal validation capabilities of artificial neural network, specifically the back-propagation algorithm which is very accurate in learning completely new systems. This research presents a combined effect of artificial neural network and fuzzy systems to accurately interpret key bus transfer parameters such as magnitude of the residual voltage, decay time, and the associated phase angle of the residual voltage in order to determine the possibility of high speed bus transfer for a particular bus and the corresponding transfer algorithm. This demonstrates potential for general applicability to improve reliability of the auxiliary power distribution system. The performance of the scheme is implemented on APR1400 nuclear power plant auxiliary system.

Keywords: auxiliary power system, bus transfer scheme, fuzzy logic, neural networks, reliability

Procedia PDF Downloads 161

Authors: Sepideh Beiramipour, Hadi Haghjouei, Kourosh Qaderi, Majid Rahimpour, Mohammad M. Ahmadi, Sameh A. Kantoush

Abstract:

Reservoir sedimentation is one of the main challenges by which the reservoir behind the dam is filled with sediments transferred through the river flow. Pressure flushing method is an effective way to drain the deposited sediments of the reservoirs through the bottom outlet. So far, several structural methods have been proposed to increase the efficiency of pressure flushing. The aim of this study is to increase the performance of Dendritic Bottomless Extended (DBE) structure on the efficiency of pressurized sediment flushing using submerged vanes. For this purpose, the physical model of the dam reservoir with dimensions of 7.5 m in length, 3.5 m in width, and 1.8 m in height in the hydraulic and water structures research laboratory of Shahid Bahonar University of Kerman was used. In order to investigate the influence of submerged vanes on the performance of DBE structure in pressure flushing, the best arrangement and geometric parameters of the vanes were selected and combined with the DBE structure. The results showed that the submerged vanes significantly increased the performance of the DBE structure so that the volume of the sediment flushing cone with the combination of two structures increased by 3.7 times compared to the DBE structure test.

Keywords: dendritic bottomless extended structure, flushing efficiency, sedimentation, sediment flushing

Procedia PDF Downloads 211

Authors: Felipe Haleyson Ribeiro dos Santos, Edson Ronaldo Guarido Filho

Abstract:

This article aims to review the literature on performance in police organizations. For that, the inOrdinatio method was adopted, which defines the form of selection and classification of articles. The search was carried out in databases, which resulted in a total of 619 documents that were cataloged and classified with the support of the Mendeley software. The theoretical scope intended here is to identify how performance in police organizations has been studied. After deepening the analysis and focusing on management, it was possible to classify the articles into three levels: individual, organizational, and institutional. However, to our best knowledge, no studies were found that addressed the performance relationship between the levels, which can be seen as a suggestion for further research.

Keywords: police management, performance, management, multi-level

Procedia PDF Downloads 103

Authors: Hamed Afrasiab, Saeed Khodaygan

Abstract:

Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis

Procedia PDF Downloads 302