Abstract: Microstructure analysis of additively manufactured (AM) materials is an important step in understanding the interrelationship between mechanical properties and materials performance. Literature on the effect of a laser-based AM process parameters on the microstructure in the substrate-deposit interface is limited. The interface region, the adjoining area of substrate and deposit, is characterized by the presence of the fusion zone (FZ) and heat affected zone (HAZ) experiencing rapid thermal gyrations resulting in thermal induced transformations. Inconel 718 was utilized as a work material for both the substrate and deposit. Three blocks of Inconel 718 material were deposited by Direct Energy Deposition (DED) using three different laser powers, 550W, 750W and 950W, respectively. A coupled thermo-mechanical transient approach was utilized to correlate temperature history to the evolution of microstructure. Thermal history of the deposition process was monitored with the thermocouples installed inside the substrate material. Interface region of the blocks were analysed with Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) including electron back-scattered diffraction (EBSD) technique. Laser power was found to influence the dissolution of intermetallic precipitated phases in the substrate and grain growth in the interface region. Microstructure and thermal history data were utilized to draw conclusive comparisons between the investigated process parameters.
Abstract: In an era when everything is increasingly digital, consumers are always looking for new options in solutions to their everyday needs. In this context, mobile apps are developing at an exponential pace. One of the fastest growing segments of mobile technologies is, obviously, e-commerce. It can be predicted that mobile commerce will record nearly three times the global growth of e-commerce across all platforms, which indicates its importance in the given segment. The current coronavirus pandemic is also changing many of the existing paradigms both socially, economically, and technologically, which has a major impact on changing consumer behavior and the emphasis on simplification and clarity of mobile solutions. This is the area that User Experience (UX) and User Interface (UI) designers deal with. Their task is to design a sufficiently attractive and interesting solution that will be available on all mobile devices and at the same time will be easy enough for the customer/visitor to get to the destination or to get the necessary information in a few clicks. The basis for changes in UX design can now be obtained not only through online analytical tools, but also through neuromarketing, especially in the case of mobile devices. The paper highlights possibilities for testing UX design applications on mobile devices using a special platform that combines a stationary eye camera (eye tracking) and facial analysis (facial coding).
Abstract: This work describes a system that uses electromyography (EMG) signals obtained from muscle sensors and an Artificial Neural Network (ANN) for signal classification and pattern recognition that is used to control a small unmanned aerial vehicle using specific arm movements. The main objective of this endeavor is the development of an intelligent interface that allows the user to control the flight of a drone beyond direct manual control. The sensor used were the MyoWare Muscle sensor which contains two EMG electrodes used to collect signals from the posterior (extensor) and anterior (flexor) forearm, and the bicep. The collection of the raw signals from each sensor was performed using an Arduino Uno. Data processing algorithms were developed with the purpose of classifying the signals generated by the arm’s muscles when performing specific movements, namely: flexing, resting, and motion of the arm. With these arm motions roll control of the drone was achieved. MATLAB software was utilized to condition the signals and prepare them for the classification. To generate the input vector for the ANN and perform the classification, the root mean square and the standard deviation were processed for the signals from each electrode. The neuromuscular information was trained using an ANN with a single 10 neurons hidden layer to categorize the four targets. The result of the classification shows that an accuracy of 97.5% was obtained. Afterwards, classification results are used to generate the appropriate control signals from the computer to the drone through a Wi-Fi network connection. These procedures were successfully tested, where the drone responded successfully in real time to the commanded inputs.
Abstract: Nowadays, Heritage Building Information Modeling (HBIM) is considered an efficient tool to represent and manage information of Cultural Heritage (CH). The basis of this tool relies on a 3D model generally obtained from a Cloud-to-BIM procedure. There are different methods to create an HBIM model that goes from manual modeling based on the point cloud to the automatic detection of shapes and the creation of objects. The selection of these methods depends on the desired Level of Development (LOD), Level of Information (LOI), Grade of Generation (GOG) as well as on the availability of commercial software. This paper presents the 3D modeling of a stone masonry chapel using Recap Pro, Revit and Dynamo interface following a three-step methodology. The first step consists of the manual modeling of simple structural (e.g., regular walls, columns, floors, wall openings, etc.) and architectural (e.g., cornices, moldings and other minor details) elements using the point cloud as reference. Then, Dynamo is used for generative modeling of complex structural elements such as vaults, infills and domes. Finally, semantic information (e.g., materials, typology, state of conservation, etc.) and pathologies are added within the HBIM model as text parameters and generic models’ families respectively. The application of this methodology allows the documentation of CH following a relatively simple to apply process that ensures adequate LOD, LOI and GOG levels. In addition, the easy implementation of the method as well as the fact of using only one BIM software with its respective plugin for the scan-to-BIM modeling process means that this methodology can be adopted by a larger number of users with intermediate knowledge and limited resources, since the BIM software used has a free student license.
Abstract: A common complaint from power system analysis students lies in the overly complex tools they need to learn and use just to simulate very basic systems or just to check the answers to power system calculations. The most basic power system studies are power-flow solutions and short-circuit calculations. This paper presents a simple tool with an intuitive interface to perform both these studies and assess its performance in comparison with existent commercial solutions. With this in mind, Elegant is a pure Python software tool for learning power system analysis developed for undergraduate and graduate students. It solves the power-flow problem by iterative numerical methods and calculates bolted short-circuit fault currents by modeling the network in the domain of symmetrical components. Elegant can be used with a user-friendly Graphical User Interface (GUI) and automatically generates human-readable reports of the simulation results. The tool is exemplified using a typical Brazilian regional system with 18 buses. This study performs a comparative experiment with 1 undergraduate and 4 graduate students who attempted the same problem using both Elegant and a commercial tool. It was found that Elegant significantly reduces the time and labor involved in basic power system simulations while still providing some insights into real power system designs.
Abstract: The main design parameter of WSN (wireless sensor network) is the energy consumption. To compensate this parameter, hierarchical clustering is a technique that assists in extending duration of the networks life by efficiently consuming the energy. This paper focuses on dealing with the WSNs and the FIS (fuzzy interface system) which are deployed to enhance the BEENISH protocol. The node energy, mobility, pause time and density are considered for the selection of CH (cluster head). The simulation outcomes exhibited that the projected system outperforms the traditional system with regard to the energy utilization and number of packets transmitted to sink.
Abstract: One of the important parts of the brain-computer interface (BCI) studies is the classification of motor imagery (MI) obtained by electroencephalography (EEG). The major goal is to provide non-muscular communication and control via assistive technologies to people with severe motor disorders so that they can communicate with the outside world. In this study, an EEG signal classification approach based on multiscale and multi-resolution transform method is presented. The proposed approach is used to decompose the EEG signal containing motor image information (right- and left-hand movement imagery). The decomposition process is performed using curvelet transform which is a multiscale and multiresolution analysis method, and the transform output was evaluated as feature data. The obtained feature set is subjected to feature selection process to obtain the most effective ones using t-test methods. SVM and k-NN algorithms are assigned for classification.
Abstract: Electroencephalography (EEG) investigations of the brain computer interfaces are based on the electrical signals resulting from neural activities in the brain. In this paper, it is offered a method for classifying motor imagery EEG signals. The suggested method classifies EEG signals into two classes using the wave atom transform, and the transform coefficients are assessed, creating the feature set. Classification is done with SVM and k-NN algorithms with and without feature selection. For feature selection t-test approaches are utilized. A test of the approach is performed on the BCI competition III dataset IIIa.
Abstract: The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.
Abstract: The fast development of technology that has advanced neuroscience and human interaction with computers has enabled solutions to various problems and issues of this new era. The Brain-Computer Interface (BCI) has opened the door to several new research areas and have been able to provide solutions to critical and vital issues such as supporting a paralyzed patient to interact with the outside world, controlling a robot arm, playing games in VR with the brain, driving a wheelchair. This review presents the state-of-the-art methods and improvements of canonical correlation analyses (CCA), an SSVEP-based BCI method. These are the methods used to extract EEG signal features or, to be said differently, the features of interest that we are looking for in the EEG analyses. Each of the methods from oldest to newest has been discussed while comparing their advantages and disadvantages. This would create a great context and help researchers understand the most state-of-the-art methods available in this field, their pros and cons, and their mathematical representations and usage. This work makes a vital contribution to the existing field of study. It differs from other similar recently published works by providing the following: (1) stating most of the main methods used in this field in a hierarchical way, (2) explaining the pros and cons of each method and their performance, (3) presenting the gaps that exist at the end of each method that can improve the understanding and open doors to new researches or improvements.
Abstract: Additive Friction Stir Manufacturing, or AFSM, is a new industrial process that follows the emergence of friction-based processes. The AFSM process is a solid-state additive process using the energy produced by the friction at the interface between a rotating non-consumable tool and a substrate. Friction depends on various parameters like axial force, rotation speed or friction coefficient. The feeder material is a metallic rod that flows through a hole in the tool. There is still a lack in understanding of the physical phenomena taking place during the process. This research aims at a better AFSM process understanding and implementation, thanks to numerical simulation and experimental validation performed on a prototype effector. Such an approach is considered a promising way for studying the influence of the process parameters and to finally identify a process window that seems relevant. The deposition of material through the AFSM process takes place in several phases. In chronological order these phases are the docking phase, the dwell time phase, the deposition phase, and the removal phase. The present work focuses on the dwell time phase that enables the temperature rise of the system due to pure friction. An analytic modeling of heat generation based on friction considers as main parameters the rotational speed and the contact pressure. Another parameter considered influential is the friction coefficient assumed to be variable, due to the self-lubrication of the system with the rise in temperature or the materials in contact roughness smoothing over time. This study proposes through a numerical modeling followed by an experimental validation to question the influence of the various input parameters on the dwell time phase. Rotation speed, temperature, spindle torque and axial force are the main monitored parameters during experimentations and serve as reference data for the calibration of the numerical model. This research shows that the geometry of the tool as well as fluctuations of the input parameters like axial force and rotational speed are very influential on the temperature reached and/or the time required to reach the targeted temperature. The main outcome is the prediction of a process window which is a key result for a more efficient process implementation.
Abstract: Shorelines are vulnerable to significant environmental impacts from oil spills. Stranded oil can cause potential short- to long-term detrimental effects along beaches that include injuries to ecosystem, socio-economic and cultural resources. In this study, a three-dimensional (3D) numerical modeling approach is developed to evaluate the fate and transport of spilled oil for hypothetical oiled shoreline cases under various combinations of beach geomorphology and environmental conditions. The developed model estimates the spatial and temporal distribution of spilled oil for the various test conditions, using the finite volume method and considering the physical transport (dispersion and advection), sinks, and sorption processes. The model includes a user-friendly interface for data input on variables such as beach properties, environmental conditions, and physical-chemical properties of spilled oil. An experimental meso-scale tank design was used to test the developed model for dissolved petroleum hydrocarbon within shorelines. The simulated results for effects of different sediment substrates, oil types, and shoreline features for the transport of spilled oil are comparable to that obtained with a commercially available model. Results show that the properties of substrates and the oil removal by shoreline effects have significant impacts on oil transport in the beach area. Sensitivity analysis, through the application of the one-step-at-a-time method (OAT), for the 3D model identified hydraulic conductivity as the most sensitive parameter. The 3D numerical model allows users to examine the behavior of oil on and within beaches, assess potential environmental impacts, and provide technical support for decisions related to shoreline clean-up operations.
Abstract: As notifications become more common through mobile devices, it is important to understand the impact of wearable devices for improved user experience of man-machine interfaces. This study examined the use of a wearable device for a real-time system using a computer simulated petrochemical system. The key research question was to determine how using information provided by the wearable device can improve human performance through measures of situational awareness and decision making. Results indicate that there was a reduction in response time when using the watch and there was no difference in situational awareness. Perception of using the watch was positive, with 83% of users finding value in using the watch and receiving haptic feedback.
Abstract: Learning styles (LSs) correspond to the individual preferences of a person regarding the modes and forms in which he/she prefers to learn throughout the teaching/learning process. The content presentation of learning objects (LOs) using knowledge about the students’ LSs offers them digital educational resources tailored to their individual learning preferences. In this context, the most relevant characteristics of the LSs along with the most appropriate forms of LOs' content presentation were mapped and associated. Such was performed in order to define the composition of an adaptive model of LO's content presentation considering the LSs, which was called Adaptation of Content Presentation of Learning Objects Considering Learning Styles (ACPLOLS). LO prototypes were created with interfaces that were adapted to students' LSs. These prototypes were based on a model created for validation of the approaches that were used, which were established through experiments with the students. The results of subjective measures of students' emotional responses demonstrated that the ACPLOLS has reached the desired results in relation to the adequacy of the LOs interface, in accordance with the Felder-Silverman LSs Model.
Abstract: In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.
Abstract: The toxicity of lead associated with the lifecycle of perovskite solar cells (PSCs( is a serious concern which may prove to be a major hurdle in the path toward their commercialization. The current proposed lead-free PSCs including Ag(I), Bi(III), Sb(III), Ti(IV), Ge(II), and Sn(II) low-toxicity cations are still plagued with the critical issues of poor stability and low efficiency. This is mainly because of their chemical stability. In the present research, utilization of all inorganic CsSnGeI3 based materials offers the advantages to enhance resistance of device to degradation, reduce the cost of cells, and minimize the carrier recombination. The presence of inorganic halide perovskite improves the photovoltaic parameters of PCSs via improved surface coverage and stability. The inverted structure of simulated devices using a 1D simulator like solar cell capacitance simulator (SCAPS) version 3308 involves TCOHTL/Perovskite/ETL/Au contact layer. PEDOT:PSS, PCBM, and CsSnGeI3 used as hole transporting layer (HTL), electron transporting layer (ETL), and perovskite absorber layer in the inverted structure for the first time. The holes are injected from highly stable and air tolerant Sn0.5Ge0.5I3 perovskite composition to HTM and electrons from the perovskite to ETL. Simulation results revealed a great dependence of power conversion efficiency (PCE) on the thickness and defect density of perovskite layer. Here the effect of an increase in operating temperature from 300 K to 400 K on the performance of CsSnGeI3 based perovskite devices is investigated. Comparison between simulated CsSnGeI3 based PCSs and similar real testified devices with spiro-OMeTAD as HTL showed that the extraction of carriers at the interfaces of perovskite absorber depends on the energy level mismatches between perovskite and HTL/ETL. We believe that optimization results reported here represent a critical avenue for fabricating the stable, low-cost, efficient, and eco-friendly all-inorganic Cs-Sn-Ge based lead-free perovskite devices.
Abstract: This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.
Abstract: In the context of the international spread of COVID-19, the Centre Scientifique et Technique du Bâtiment (CSTB) has led a joint research with the French government authorities Hauts-de-Seine department, to analyse the risk in school spaces according to their configuration, ventilation system and spatial segmentation strategy. This paper describes the main results of this joint research. A multidisciplinary team involving experts in indoor air quality/ventilation, pedestrian movements and IT domains was established to develop a COVID risk analysis tool based on Building Information Model. The work started with specific analysis on two pilot schools in order to provide for the local administration specifications to minimize the spread of the virus. Different recommendations were published to optimize/validate the use of ventilation systems and the strategy of student occupancy and student flow segmentation within the building. This COVID expertise has been digitized in order to manage a quick risk analysis on the entire building that could be used by the public administration through an easy user interface implemented in a free BIM Management software. One of the most interesting results is to enable a dynamic comparison of different ventilation system scenarios and space occupation strategy inside the BIM model. This concurrent engineering approach provides users with the optimal solution according to both ventilation and pedestrian flow expertise.
Abstract: International guidelines recommend removing any
artificial body in Low Earth Orbit (LEO) within 25 years from
mission completion. Among disposal strategies, electrodynamic
tethers appear to be a promising option for LEO, thanks to the
limited storage mass and the minimum interface requirements to the
host spacecraft. In particular, recent technological advances make it
feasible to deorbit large objects with tether lengths of a few kilometers
or less. To further investigate such an innovative passive system,
the European Union is currently funding the project E.T.PACK
– Electrodynamic Tether Technology for Passive Consumable-less
Deorbit Kit in the framework of the H2020 Future Emerging
Technologies (FET) Open program. The project focuses on the design
of an end of life disposal kit for LEO satellites. This kit aims to
deploy a taped tether that can be activated at the spacecraft end of life
to perform autonomous deorbit within the international guidelines.
In this paper, the orbital performance of the E.T.PACK deorbiting
kit is compared to other disposal methods. Besides, the orbital decay
prediction is parametrized as a function of spacecraft mass and tether
system performance. Different values of length, width, and thickness
of the tether will be evaluated for various scenarios (i.e., different
initial orbital parameters). The results will be compared to other
end-of-life disposal methods with similar allocated resources. The
analysis of the more innovative system’s performance with the tape
coated with a thermionic material, which has a low work-function
(LWT), for which no active component for the cathode is required,
will also be briefly discussed. The results show that the electrodynamic tether option can be a
competitive and performant solution for satellite disposal compared
to other deorbit technologies.
Abstract: There is a real danger that both practitioners and researchers considering risks associated with megaprojects ignore or underestimate the impacts of cultural risk. The paper investigates the potential impacts of a failure to achieve cultural unity between the principal actors executing a megaproject. The principle relationships include the relationships between the principle Contractors and the project stakeholders or the project stakeholders and their principle advisors, Western Consultants. This study confirms that cultural dissonance between these parties can delay or disrupt the megaproject execution and examines why cultural issues should be prioritized as a significant risk factor in megaproject delivery. This paper addresses the practical impacts and potential mitigation measures, which may reduce cultural dissonance for a megaproject's delivery. This information is retrieved from on-going case studies in live infrastructure megaprojects in Europe and the Middle East's GCC states, from Western Consultants' perspective. The collaborating researchers each have at least 30 years of construction experience and are engaged in architecture, project management and contracts management, dealing with megaprojects in Europe or the GCC. After examining the cultural interfaces they have observed during the execution of megaprojects, they conclude that globally, culture significantly influences their efficient delivery. The study finds that cultural risk is ever-present, where different nationalities co-manage megaprojects and that cultural conflict poses a real threat to the timely delivery of megaprojects. The study indicates that the higher the cultural distance between the principal actors, the more pronounced the risk, with the risk of cultural dissonance more prominent in GCC megaprojects. The findings support a more culturally aware and cohesive team approach and recommend cross-cultural training to mitigate the effects of cultural disparity.