Search results for: edge computing module

Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

Procedia PDF Downloads 202

Authors: Shuen-Tai Wang, Yu-Ching Lin

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With more and more workflow systems adopting cloud as their execution environment, it presents various challenges that need to be addressed in order to be utilized efficiently. This paper introduces a method for resource provisioning based on our previous research of dynamic allocation and its pipeline processes. We present an abstraction for workload scheduling in which independent tasks get scheduled among various available processors of distributed computing for optimization. We also propose an integrated web-based workflow designer by taking advantage of the HTML5 technology and chaining together multiple tools. In order to make the combination of multiple pipelines executing on the cloud in parallel, we develop a script translator and an execution engine for workflow management in the cloud. All information is known in advance by the workflow engine and tasks are allocated according to the prior knowledge in the repository. This proposed effort has the potential to provide support for process definition, workflow enactment and monitoring of workflow processes. Users would benefit from the web-based system that allows creation and execution of pipelines without scripting knowledge.

Keywords: workflow systems, resources provisioning, workload scheduling, web-based, workflow engine

Procedia PDF Downloads 160

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

Procedia PDF Downloads 148

Authors: Pascal Mwenge, Tumisang Seodigeng

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The combination of world population and the third industrial revolution led to high demand for fuels. On the other hand, the decrease of global fossil 8fuels deposits and the environmental air pollution caused by these fuels has compounded the challenges the world faces due to its need for energy. Therefore, new forms of environmentally friendly and renewable fuels such as biodiesel are needed. The primary analytical techniques for methanolysis yield monitoring have been chromatography and spectroscopy, these methods have been proven reliable but are more demanding, costly and do not provide real-time monitoring. In this work, the in situ monitoring of biodiesel from sunflower oil using FTIR (Fourier Transform Infrared) has been studied; the study was performed using EasyMax Mettler Toledo reactor equipped with a DiComp (Diamond) probe. The quantitative monitoring of methanolysis was performed by building a quantitative model with multivariate calibration using iC Quant module from iC IR 7.0 software. 15 samples of known concentrations were used for the modelling which were taken in duplicate for model calibration and cross-validation, data were pre-processed using mean centering and variance scale, spectrum math square root and solvent subtraction. These pre-processing methods improved the performance indexes from 7.98 to 0.0096, 11.2 to 3.41, 6.32 to 2.72, 0.9416 to 0.9999, RMSEC, RMSECV, RMSEP and R2Cum, respectively. The R² value of 1 (training), 0.9918 (test), 0.9946 (cross-validation) indicated the fitness of the model built. The model was tested against univariate model; small discrepancies were observed at low concentration due to unmodelled intermediates but were quite close at concentrations above 18%. The software eliminated the complexity of the Partial Least Square (PLS) chemometrics. It was concluded that the model obtained could be used to monitor methanol of sunflower oil at industrial and lab scale.

Keywords: biodiesel, calibration, chemometrics, methanolysis, multivariate analysis, transesterification, FTIR

Procedia PDF Downloads 148

Authors: Zahzouh Zoubir

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The widespread adoption of photovoltaic panel systems for global electricity generation is a prominent trend. Algeria, demonstrating steadfast commitment to strategic development and innovative projects for harnessing solar energy, emerges as a pioneering force in the field. Heat and radiation, being fundamental factors in any solar system, are currently subject to comprehensive studies aiming to discern their genuine impact on crucial elements within photovoltaic systems. This endeavor is particularly pertinent given that solar module performance is exclusively assessed under meticulously defined Standard Test Conditions (STC). Nevertheless, when deployed outdoors, solar modules exhibit efficiencies distinct from those observed under STC due to the influence of diverse environmental factors. This discrepancy introduces ambiguity in performance determination, especially when surpassing test conditions. This article centers on the performance monitoring of an Algerian photovoltaic project, specifically the Oued El Keberite power (OKP) plant boasting a 15 megawatt capacity, situated in the town of Souk Ahras in eastern Algeria. The study elucidates the behavior of a subfield within this facility throughout the year, encompassing various conditions beyond the STC framework. To ensure the optimal efficiency of solar panels, this study integrates crucial factors, drawing on an authentic technical sheet from the measurement station of the OKP photovoltaic plant. Numerical modeling and simulation of a sub-field of the photovoltaic station were conducted using MATLAB Simulink. The findings underscore how radiation intensity and temperature, whether low or high, impact the short-circuit current, open-circuit voltage; fill factor, and overall efficiency of the photovoltaic system.

Keywords: performance monitoring, photovoltaic system, numerical modeling, radiation intensity

Procedia PDF Downloads 69

Authors: Solanki Ravirajsinh, Kudo Kuniaki, Sharma Ankit, Devi Sherine, Kuboshima Misaki, Tachi Shuntaro

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In software development life cycles, the absence of scalable CI/CD significantly impacts organizations, leading to increased overall maintenance costs, prolonged release delivery times, heightened manual efforts, and difficulties in meeting tight deadlines. Implementing CI/CD with standard serverless technologies using cloud services overcomes all the above-mentioned issues and helps organizations improve efficiency and faster delivery without the need to manage server maintenance and capacity. By integrating scalable CI/CD with scalable automation testing, productivity, quality, and agility are enhanced while reducing the need for repetitive work and manual efforts. Implementing scalable CI/CD for development using cloud services like ECS (Container Management Service), AWS Fargate, ECR (to store Docker images with all dependencies), Serverless Computing (serverless virtual machines), Cloud Log (for monitoring errors and logs), Security Groups (for inside/outside access to the application), Docker Containerization (Docker-based images and container techniques), Jenkins (CI/CD build management tool), and code management tools (GitHub, Bitbucket, AWS CodeCommit) can efficiently handle the demands of diverse development environments and are capable of accommodating dynamic workloads, increasing efficiency for faster delivery with good quality. CI/CD pipelines encourage collaboration among development, operations, and quality assurance teams by providing a centralized platform for automated testing, deployment, and monitoring. Scalable CI/CD streamlines the development process by automatically fetching the latest code from the repository every time the process starts, building the application based on the branches, testing the application using a scalable automation testing framework, and deploying the builds. Developers can focus more on writing code and less on managing infrastructure as it scales based on the need. Serverless CI/CD eliminates the need to manage and maintain traditional CI/CD infrastructure, such as servers and build agents, reducing operational overhead and allowing teams to allocate resources more efficiently. Scalable CI/CD adjusts the application's scale according to usage, thereby alleviating concerns about scalability, maintenance costs, and resource needs. Creating scalable automation testing using cloud services (ECR, ECS Fargate, Docker, EFS, Serverless Computing) helps organizations run more than 500 test cases in parallel, aiding in the detection of race conditions, performance issues, and reducing execution time. Scalable CI/CD offers flexibility, dynamically adjusting to varying workloads and demands, allowing teams to scale resources up or down as needed. It optimizes costs by only paying for the resources as they are used and increases reliability. Scalable CI/CD pipelines employ automated testing and validation processes to detect and prevent errors early in the development cycle.

Keywords: achieve parallel execution, cloud services, scalable automation testing, scalable continuous integration and deployment

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Authors: Sandhra M. Pillai

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This article examines the relevance, difficulties, and potential applications of DNA analysis in the criminal justice system. A potent tool for connecting suspects to crime sites, clearing the innocent of wrongdoing, and resolving cold cases, DNA analysis has transformed forensic investigations. The scientific foundations of DNA analysis, including DNA extraction, sequencing, and statistical analysis, are covered in the article. To guarantee accurate and trustworthy findings, it also discusses the significance of quality assurance procedures, chain of custody, and DNA sample storage. DNA analysis has significantly advanced science, but it also brings up substantial moral and legal issues. To safeguard individual rights and uphold public confidence, privacy concerns, possible discrimination, and abuse of DNA information must be properly addressed. The paper also emphasises the effects of the criminal justice system on people and communities while highlighting the necessity of equity, openness, and fair access to DNA testing. The essay describes the obstacles and future directions for DNA analysis. It looks at cutting-edge technology like next-generation sequencing, which promises to make DNA analysis quicker and more affordable. To secure the appropriate and informed use of DNA evidence, it also emphasises the significance of multidisciplinary collaboration among scientists, law enforcement organisations, legal experts, and policymakers. In conclusion, DNA analysis has enormous potential for improving the course of criminal justice. We can exploit the potential of DNA technology while respecting the ideals of justice, fairness, and individual rights by navigating the ethical, legal, and societal issues and encouraging discussion and collaboration.

Keywords: DNA analysis, DNA evidence, reliability, validity, legal frame, admissibility, ethical considerations, impact, future direction, challenges

Procedia PDF Downloads 64

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

Procedia PDF Downloads 89

Authors: Ahmed Tarek, Ahmed Alveed

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Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success.

Keywords: coordinate-based optimal routing, Hamiltonian Circuit, heuristic algorithm, traveling salesman problem, vehicle routing problem

Procedia PDF Downloads 147

Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

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Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

Procedia PDF Downloads 265

Authors: H. K. Sachidananda, K. Raghunandana, B. Shivamurthy

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The estimation of gear tooth stiffness is important for finding the load distribution between the gear teeth when two consecutive sets of teeth are in contact. Based on dynamic model a C-program has been developed to compute mesh stiffness. By using this program position dependent mesh stiffness of spur gear tooth for various profile shifts have been computed for a fixed center distance and altering tooth-sum gearing (100 by ± 4%). It is found that the C-program using dynamic model is one of the rapid soft computing technique which helps in design of gears. The mesh tooth stiffness along the path of contact is studied for both 20° and 25° pressure angle gears at various profile shifts. Better tooth stiffness is noticed in case of negative alteration tooth-sum gears compared to standard and positive alteration tooth-sum gears. Also, in case of negative alteration tooth-sum gearing better mesh stiffness is noticed in 20° pressure angle when compared to 25°.

Keywords: altered tooth-sum gearing, bending fatigue, mesh stiffness, spur gear

Procedia PDF Downloads 325

Authors: Cihan Çetinkaya, Eren Özceylan, Kerem Elibal

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This paper presents a solution method for selection of production facility. The motivation has been taken from a real life case, an automotive subcontractor which has two production facilities at different cities and parts. The problem is to decide which part(s) should be produced at which facility. To the best of our knowledge, until this study, there was no scientific approach about this problem at the firm and decisions were being given intuitively. In this study, some logistic cost parameters have been defined and with these parameters a mathematical model has been constructed. Defined and collected cost parameters are handling cost of parts, shipment cost of parts and shipment cost of welding fixtures. Constructed multi-objective mathematical model aims to minimize these costs while aims to balance the workload between two locations. Results showed that defined model can give optimum solutions in reasonable computing times. Also, this result gave encouragement to develop the model with addition of new logistic cost parameters.

Keywords: automotive subcontract, facility assignment, logistic costs, multi-objective models

Procedia PDF Downloads 366

Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

Procedia PDF Downloads 196

Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

Procedia PDF Downloads 103

Authors: Ashfaq Ahmed Kharal

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The study asserts that environment-friendly practices are increasingly being used by businesses throughout the world. Today, there is a great deal of interest in green management from both practitioners and academics. People expect managers to use resources intelligently and responsibly and to minimize the use of water, minerals, and other components in the finished products, as a crucial factor in this passion. The ethical or moral relevance of green management cannot be overstated. Employee Green Behavior (EGB) and environmental sustainability were shown to be significantly influenced by green human resource management (GHRM) in this study. Environmental issues, such as climate change, global warming, and resource conservation have a direct impact on business activities. The environment, society, and economy all suffer as a result of such obstacles. The depletion of natural resources needs immediate replenishment. As a result of government, non-governmental organizations (NGOs), environmental activists, and labor unions putting pressure on businesses and firms are now required to operate in ecologically responsible ways. Organizations are increasingly concerned about environmental sustainability in light of contemporary environmental circumstances and commercial marketplaces. Companies that emphasize long-term viability will benefit from integrating green employee behavior, green human resource management techniques, and environmental sustainability. Competition drives firms to respond to external causes, adapt, and evolve in response to changing conditions in the marketplace. Organizations develop strategic capabilities to transform their resources and acquire a competitive edge while implementing a business plan. The study of GHRM's function is being prioritized since environmental sustainability is becoming a more important strategic goal.

Keywords: EGB, GHRM, environment sustainability, green ecology

Procedia PDF Downloads 105

Authors: Gulcan Ozerim, Gunay Anlas

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In shape memory alloys, upon loading, stress increases around crack tip and a martensitic phase transformation occurs in early stages. In many studies the stress distribution in the vicinity of the crack tip is represented by using linear elastic fracture mechanics (LEFM) although the pseudo-elastic behavior results in a nonlinear stress-strain relation. In this study, the HRR singularity (Hutchinson, Rice and Rosengren), that uses Rice’s path independent J-integral, is tried to formulate the stress distribution around the crack tip. In HRR approach, the Ramberg-Osgood model for the stress-strain relation of power-law hardening materials is used to represent the elastic-plastic behavior. Although it is recoverable, the inelastic portion of the deformation in martensitic transformation (up to the end of transformation) resembles to that of plastic deformation. To determine the constants of the Ramberg-Osgood equation, the material’s response is simulated in ABAQUS using a UMAT based on ZM (Zaki-Moumni) thermo-mechanically coupled model, and the stress-strain curve of the material is plotted. An edge cracked shape memory alloy (Nitinol) plate is loaded quasi-statically under mode I and modeled using ABAQUS; the opening stress values ahead of the cracked tip are calculated. The stresses are also evaluated using the asymptotic equations of both LEFM and HRR. The results show that in the transformation zone around the crack tip, the stress values are much better represented when the HRR singularity is used although the J-integral does not show path independent behavior. For the nodes very close to the crack tip, the HRR singularity is not valid due to the non-proportional loading effect and high-stress values that go beyond the transformation finish stress.

Keywords: crack, HRR singularity, shape memory alloys, stress distribution

Procedia PDF Downloads 325

Authors: Ahmed Al-Adaileh, Souheil Khaddaj

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Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

Procedia PDF Downloads 196

Authors: Bhavini Pandya

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This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

Procedia PDF Downloads 188

Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

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Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

Procedia PDF Downloads 157

Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

Procedia PDF Downloads 69

Authors: Gerardo Casal, Miguel E. Vázquez-Méndez

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The clothoid (also known as Cornu spiral or Euler spiral) is a curve that is characterized because its curvature is proportional to its length. This property makes that it would be widely used as transition curve for designing the layout of roads and railway tracks. In this work, from the geometrical property characterizing the clothoid, its parametric equations are obtained and two algorithms to compute it are compared. The first (classical), is widely used in Surveying Schools and it is based on the use of explicit formulas obtained from Taylor expansions of sine and cosine functions. The second one (alternative) is a very simple algorithm, based on the numerical solution of the initial value problems giving the clothoid parameterization. Both methods are compared in some typical surveying problems. The alternative method does not use complex formulas and so it is conceptually very simple and easy to apply. It gives good results, even if the classical method goes wrong (if the quotient between length and radius of curvature is high), needs no subsequent translations nor rotations and, consequently, it seems an efficient tool for designing the layout of roads and railway tracks.

Keywords: transition curves, railroad and highway engineering, Runge-Kutta methods

Procedia PDF Downloads 283

Authors: Rozo D. Juan Felipe, Ramírez L. Leonardo Juan, Puerta A. Gabriel Alberto

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The telemedicine services require correct computing resource management to guarantee productivity and efficiency for medical and non-medical staff. The aim of this study was to examine web management strategies to ensure the availability of resources and services in telemedicine so as to provide medical information management with an accessible strategy. In addition, to evaluate the quality-of-service parameters, the followings were measured: delays, throughput, jitter, latency, available bandwidth, percent of access and denial of services based of web management performance map with profiles permissions and database management. Through 24 different test scenarios, the results show 100% in availability of medical information, in relation to access of medical staff to web services, and quality of service (QoS) of 99% because of network delay and performance of computer network. The findings of this study suggest that the proposed strategy of web management is an ideal solution to guarantee the availability, reliability, and accessibility of medical information. Finally, this strategy offers seven user profile used at telemedicine center of Bogota-Colombia keeping QoS parameters suitable to telemedicine services.

Keywords: availability, medical information, QoS, strategy, telemedicine

Procedia PDF Downloads 205

Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

Procedia PDF Downloads 175

Authors: Alexander Kospach, Fabian Benezeder, Jürgen Abraham

Abstract:

E-mobility poses new challenges for inverters (e.g., higher switching frequencies) in terms of thermal behavior and thermal management. Due to even higher switching frequencies, thermal losses become greater, and the cooling of critical components (like insulated gate bipolar transistor and diodes) comes into focus. New manufacturing methods, such as 3D printing, enable completely new pin-fin structures that can handle higher waste heat to meet the new thermal requirements. Based on the geometrical specifications of the industrial partner regarding the manufacturing possibilities for 3D printing, different and completely new pin-fin structures were numerically investigated for their hydraulic and thermal behavior in fundamental studies assuming an indirect liquid cooling. For the 3D computational fluid dynamics (CFD) thermal simulations OpenFOAM was used, which has as numerical method the finite volume method for solving the conjugate heat transfer problem. A steady-state solver for turbulent fluid flow and solid heat conduction with conjugate heat transfer between solid and fluid regions was used for the simulations. In total, up to fifty pinfin structures and arrangements, some of them completely new, were numerically investigated. On the basis of the results of the principal investigations, the best two pin-fin structures and arrangements for the complete module cooling of an automotive inverter were numerically investigated and compared. There are clear differences in the maximum temperatures for the critical components, such as IGTBs and diodes. In summary, it was shown that 3D pin fin structures can significantly contribute to the improvement of heat transfer and cooling of an automotive inverter. This enables in the future smaller cooling designs and a better lifetime of automotive inverter modules. The new pin fin structures and arrangements can also be applied to other cooling applications where 3D printing can be used.

Keywords: pin fin heat sink optimization, 3D printed pin fins, CFD simulation, power electronic cooling, thermal management

Procedia PDF Downloads 102

Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

Abstract:

Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

Procedia PDF Downloads 148

Authors: Ahmed Elkholy

Abstract:

Most power transmission cases use gearing in general, and worm gearing, in particular for energy utilization. Therefore, designing gears for minimum weight and maximum power transmission is the main target of this study. In this regard, a new approach has been developed to estimate the load share and stress distribution of worm gear sets. The approach is based upon considering the instantaneous tooth meshing stiffness where the worm gear drive was modelled as a series of spur gear slices, and each slice was analyzed separately using a well-established criteria. By combining the results obtained for all slices, the entire worm gear set loading and stressing was determined. The geometric modelling method presented, allows tooth elastic deformation and tooth root stresses of worm gear drives under different load conditions to be investigated. On the basis of the method introduced in this study, the instantaneous meshing stiffness and load share were obtained. In comparison with existing methods, this approach has both good analytical accuracy and less computing time.

Keywords: gear, load/stress distribution, worm, wheel, tooth stiffness, contact line

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Authors: Hanjun Yun, Gun Bae, Nabin Paul, Cheolhee Moon

Abstract:

Flexible OLED is an important technology for the next generation display over various kinds of applications. However, the organic materials of OLEDs degrade rapidly under the invasion of oxygen and water moisture. The degradation causes the formation of non-emitting areas which gradually suppress the device brightness, ultimately the lifetime of the device decreasing rapidly. Until now, the most suitable sealing process of the flexible OLED devices is a thin film encapsulation (TFE). However, TFE consists of a multilayer thin-film structure with organic-inorganic materials, so the cost is expensive and the process time is long. Another problem is that the blocking characteristics from the moisture and oxygen are not perfect. Therefore, the encapsulation of the flexible OLED device is a still key technical issue for the successful market entry. In this study, we are to introduce an auxiliary sealing line between the two flexible substrates. The electrode lines were formed on the substrates which have a SiNx barrier coating layer. To induce the solid phase diffusion process between the SiNx layer and the electrode lines, the electrode materials were determined as Al-Si composition. Thermal energy was supplied for both the SiNx layer and Al-Si electrode lines within the furnace to induce the interfacial bonding through the solid phase diffusion of Si. We printed a test pattern for the edge of the flexible PET substrate of 3cm*3cm size. Experimental conditions such as heating temperature, heating time were optimized to get enough adhesion strength which was estimated through the competitive bending test. Finally, OLED devices with flexible PET substrate of 3cm*3cm size were manufactured to investigate the blocking characteristics as an encapsulation layer.

Keywords: barrier, encapsulation, OLED, solid phase diffusion

Procedia PDF Downloads 237

Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

Abstract:

Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval

Procedia PDF Downloads 131

Authors: Sneha Kumari, Ravi Krishnan Elangovan

Abstract:

This abstract describes the development of a high-speed tracking method by modification in motor components for nanoparticle attachment. Myosin motors are nano-sized protein machines powering movement that defines life. These miniature molecular devices serve as engines utilizing chemical energy stored in ATP to produce useful mechanical energy in the form of a few nanometre displacement events leading to force generation that is required for cargo transport, cell division, cell locomotion, translated to macroscopic movements like running etc. With the advent of in vitro motility assay (IVMA), detailed functional studies of the actomyosin system could be performed. The major challenge with the currently available IVMA for tracking actin filaments is a resolution limitation of ± 50nm. To overcome this, we are trying to develop Single Molecule IVMA in which nanoparticle (GNP/QD) will be attached along or on the barbed end of actin filaments using CapZ protein and visualization by a compact TIRF module called ‘cTIRF’. The waveguide-based illumination by cTIRF offers a unique separation of excitation and collection optics, enabling imaging by scattering without emission filters. So, this technology is well equipped to perform tracking with high precision in temporal resolution of 2ms with significantly improved SNR by 100-fold as compared to conventional TIRF. Also, the nanoparticles (QD/GNP) attached to actin filament act as a point source of light coffering ease in filament tracking compared to conventional manual tracking. Moreover, the attachment of cargo (QD/GNP) to the thin filament paves the way for various nano-technological applications through their transportation to different predetermined locations on the chip

Keywords: actin, cargo, IVMA, myosin motors and single-molecule system

Procedia PDF Downloads 87

Authors: Mohamed H. Sorour, Hayam F. Shaalan, Heba A. Hani, Mahmoud A. El-Toukhy

Abstract:

This paper reports the results of nanofiltration (NF) polymeric membrane for the recovery of divalent ions (calcium and magnesium) from Red Seawater. Pilot plant experiments have been carried out using Alfa-Laval (NF 2517/48) membrane module. System was operated in both total recirculation mode (permeate and brine) and brine recirculation mode under hydraulic pressure of 15 bar. Impacts of some chelating agents on both flux and rejection have been also investigated. Results indicated that pure water permeability ranges from 17 to 85.5 L/m²h at 2-15 bar. Comparison with seawater permeability under the same operating pressure values reveals lower values of 8.9-31 L/m²h manifesting the effect of the osmotic pressure of seawater. Overall total dissolved solids (TDS) reduction was almost constant without incorporation of chelating agents. On the contrary of expectations, the use of chelating agents N-(2-hydroxyethyl) ethylene diamine-N,N´,N´-triacetic acid (HEDTA) and ethylene glycol bis (2-aminoethyl ether)-N,N,N´,N´-tetraacetic acid (EGTA) showed flux decline of about 3-15%. Analysis of rejection data of total recirculation mode showed reasonable rejection values of 35%, 59% and 90% for Ca, Mg and SO₄, respectively. Operating under brine recirculation mode only showed a decrease of rejection to 33%, 56% and 86% for Ca, Mg and SO₄, respectively. The use of chelating agents has no substantial effect on NF membrane performance except for increasing the total Ca rejection to 48 and 65% for EGTA and HEDTA, respectively. Results, in general, confirmed the powerful separation of NF technology for softening and recovery of divalent ions from seawater. It is anticipated that increasing operating pressure beyond the limits of our investigations would improve the rejection and flux values. A trade-off should be considered between operating cost (due to higher pressure and marginal benefits as manifested by expected improved performance). The experimental results fit well with the formulated rejection empirical correlations and the published ones.

Keywords: nanofiltration, seawater, recovery, calcium, magnesium

Procedia PDF Downloads 165