Search results for: inverse synthetic aperture radar

Authors: C.W. Kan

Abstract:

This paper investigates the use of atmospheric pressure plasma for improving the surface hydrophobicity of polyurethane synthetic leather with tetramethylsilane (TMS). The atmospheric pressure plasma treatment with TMS is a single-step process to enhance the hydrophobicity of polyurethane synthetic leather. The hydrophobicity of the treated surface was examined by contact angle measurement. The physical and chemical surface changes were evaluated by scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). The purpose of this paper is to provide learning materials for understanding how to use atmospheric pressure plasma in the textile finishing process to transform a hydrophilic surface to hydrophobic.

Keywords: Learning materials, atmospheric pressure plasma treatment, hydrophobic, hydrophilic, surface

Procedia PDF Downloads 353

Authors: Gokhan Sir, Muhammed Ergun, Onder Balioglu

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Nowadays, laundry dryers containing heaters and heat pumps are used to provide fast and efficient drying. In this system, as the drying capacity changes, the sensible and latent heat transfer rate in the evaporator changes. Therefore, the drying time measured for the unit capacity increases as the drying capacity decreases. The objective of this study is to investigate the evaporator efficiency according to load amount and textile type in hybrid heat pump dryers. Air side flow rate and system temperatures (air side and refrigeration side) were monitored instantly, and the specific moisture extraction rate (SMER), evaporator efficiency, and heat transfer mechanism between the textile and hybrid heat pump system were examined. Evaporator efficiency of heat pump dryers for cotton and synthetic based textile types in load amounts of 2, 5, 8 and 10 kg were investigated experimentally. As a result, the maximum evaporator efficiency (%72) was obtained in drying cotton and synthetic based textiles with a capacity of 5 kg; the minimum evaporator efficiency (%40) was obtained in drying cotton and synthetic based textiles with a capacity of 2 kg. The experimental study also reveals that capacity-dependent flow rate changes are the major factor for evaporator efficiency.

Keywords: evaporator, heat pump, hybrid, laundry dryer, textile

Procedia PDF Downloads 139

Authors: Abbasali Abouei Mehrizi, Hamid Hassanzadeh Afrouzi

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Reducing the drag force increases the efficiency of the aircraft and its better performance. Flow control methods delay the phenomenon of flow separation and consequently reduce the reversed flow phenomenon in the separation region and enhance the performance of the lift force while decreasing the drag force and thus improving the aircraft efficiency. Flow control methods can be divided into active and passive types. The use of synthetic jets actuator (SJA) used in this study for NACA GA (W) -2 airfoil is one of the active flow control methods to prevent stall phenomenon on the airfoil. In this research, the relevant airfoil in different angles of attack with and without jets has been compared by OpenFOAM. Also, after achieving the proper SJA position on the airfoil suction surface, the simultaneous effect of two SJAs has been discussed. It was found to have the best effect at 12% chord (C), close to the airfoil’s leading edge (LE). At 12% chord, SJA decreases the drag significantly with increasing lift, and also, the average lift increase was higher than other situations and was equal to 10.4%. The highest drag reduction was about 5% in SJA=0.25C. Then, due to the positive effects of SJA in the 12% and 25% chord regions, these regions were considered for applying dual jets in two post-stall angles of attack, i.e., 16° and 22°.

Keywords: active and passive flow control methods, computational fluid dynamics, flow separation, synthetic jet

Procedia PDF Downloads 83

Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

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Authors: R. Furutani, K. Ishii

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Laser interferometers are now widely used for length and displacement measurement. In conventional methods, the optical path difference between two mirrors, one of which is a reference mirror and the other is a target mirror, is measured, as in Michelson interferometry, or two target mirrors are set up and the optical path difference between the two targets is measured, as in differential interferometry. In these interferometers, the two laser beams pass through different optical elements so that the measurement result is affected by the vibration and other effects in the optical paths. In addition, it is difficult to measure the roll angle around the optical axis. The proposed interferometer simultaneously measures both the translational motion along the optical axis and the roll motion around it by combining the retroreflective principle of the ball lens (BL) and the polarization. This interferometer detects the interferogram by the two beams traveling along the identical optical path from the beam source to BL. This principle is expected to reduce external influences by using the interferogram between the two lasers in an identical optical path. The proposed interferometer uses a BL so that the reflected light from the lens travels on the identical optical path as the incident light. After reaching the aperture of the He-Ne laser oscillator, the reflected light is reflected by a mirror with a very high reflectivity installed in the aperture and is irradiated back toward the BL. Both the first laser beam that enters the BL and the second laser beam that enters the BL after the round trip interferes with each other, enabling the measurement of displacement along the optical axis. In addition, for the measurement of the roll motion, a quarter-wave plate is installed on the optical path to change the polarization state of the laser. The polarization states of the first laser beam and second laser beam are different by the roll angle of the target. As a result, this system can measure the displacement and the roll angle of BL simultaneously. It was verified by the simulation and the experiment that the proposed optical system could measure the displacement and the roll angle simultaneously.

Keywords: common path interferometer, displacement measurement, laser interferometer, simultaneous measurement, roll angle measurement

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Authors: Solaine Hachem, Frédéric Bourquin, Dominique Siegert

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The brutal collapse of bridges is mainly due to scour. Indeed, the soil erosion in the riverbed around a pier modifies the embedding conditions of the structure, reduces its overall stiffness and threatens its stability. Hence, finding an efficient technique that allows early scour detection becomes mandatory. Vibration analysis is an indirect method for scour detection that relies on real-time monitoring of the bridge. It tends to indicate the presence of a scour based on its consequences on the stability of the structure and its dynamic response. Most of the research in this field has focused on the dynamic behavior of a single pile and has examined the depth of the scour. In this paper, a bridge is fully modeled with all piles and spans and the scour is represented by a reduction in the foundation's stiffnesses. This work aims to identify the vibration modes sensitive to the rigidity’s loss in the foundations so that their variations can be considered as a scour indicator: the decrease in soil-structure interaction rigidity leads to a decrease in the natural frequencies’ values. By using the first-order perturbation method, the expression of sensitivity, which depends only on the selected vibration modes, is established to determine the deficiency of foundations stiffnesses. The solutions are obtained by using the singular value decomposition method for the regularization of the inverse problem. The propagation of uncertainties is also calculated to verify the efficiency of the inverse problem method. Numerical simulations describing different scenarios of scour are investigated on a simplified model of a real composite steel-concrete bridge located in France. The results of the modal analysis show that the modes corresponding to in-plane and out-of-plane piers vibrations are sensitive to the loss of foundation stiffness. While the deck bending modes are not affected by this damage.

Keywords: bridge’s piers, inverse problems, modal sensitivity, scour detection, vibration analysis

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Authors: Abolfazl Zaraki, Yoshikatsu Hayashi, Harry Thorpe, Vincent Strong, Gisle-Andre Larsen, William Holderbaum

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Thanks to the high maneuverability of the cable-driven hyper-redundant manipulators (HRMs), this class of robots has shown a superior capability in highly confined and unstructured space applications. Although the large number of degrees of freedom (DOF) of HRMs enhances the motion flexibility and the robot’s reachability range, it highly increases the complexity of the kinematic configuration which makes the kinematic control problem very challenging or even impossible to solve. This paper presents our current progress achieved on the development of a kinematic-based leader-follower control system which is designed to control not only the robot’s body posture but also to control the trajectory of the robot’s movement in a semi-autonomous manner (the human operator is retained in the robot’s control loop). To obtain the forward kinematic model, the coordinate frames are established by the classical Denavit–Hartenburg (D-H) convention for a hyper-redundant serial manipulator which has a controlled cables-driven mechanism. To solve the inverse kinematics of the robot, unlike the conventional methods, a leader-follower mechanism, based on the sequential inverse kinematic, is followed. Using this mechanism, the inverse kinematic problem is solved for all sequential joints starting from the head joint to the base joint of the robot. To verify the kinematic design and simulate the robot motion, the MATLAB robotic toolbox is used. The simulation result demonstrated the promising capability of the proposed leader-follower control system in controlling the robot motion and trajectory in our confined space application.

Keywords: hyper-redundant robots, kinematic analysis, semi-autonomous control, serial manipulators

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Authors: A. Boveiri Monji, H. Yousefnia, S. Zolghadri, B. Salimi

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The main goal of this paper was to make use of green reagents as a substitute of perilous synthetic reagents and organic solvents for spectrophotometric determination of hafnium(IV). The extracts taken from six different kinds of tree leaves including Acer negundo, Ficus carica, Cerasus avium, Chimonanthus, Salix babylonica and Pinus brutia, were applied as green reagents for the experiments. In 6-M hydrochloric acid, hafnium reacted with the reagent to form a yellow product and showed maximum absorbance at 421 nm. Among tree leaves, Chimonanthus showed satisfactory results with a molar absorptivity value of 0.61 × 10⁴ l mol^-1 cm^-1 and the method was linear in the 0.3-9 µg mL ^-1 concentration range. The detection limit value was 0.064 µg mL^-1. The proposed method was simple, low cost, clean, and selective.

Keywords: hafnium, spectrophotometric determination, synthetic reagents, tree leaves

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Authors: Vanessa Funk, Steven Blum, Stephanie Cole, Jorge Maciel, Matthew Lux

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Paper-based synthetic gene circuits offer a new paradigm for programmable, fieldable biodetection. We demonstrate that by freeze-drying gene circuits with in vitro expression machinery, we can use complimentary RNA sequences to trigger colorimetric changes upon rehydration. We have successfully utilized both green fluorescent protein and luciferase-based reporters for easy visualization purposes in solution. Through several efforts, we are aiming to use this new platform technology to address a variety of needs in portable detection by demonstrating several more expression and reporter systems for detection functions on paper. In addition to RNA-based biodetection, we are exploring the use of various mechanisms that cells use to respond to environmental conditions to move towards all-hazards detection. Examples include explosives, heavy metals for water quality, and toxic chemicals.

Keywords: cell-free lysates, detection, gene circuits, in vitro

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Authors: Andrey Khalov

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The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments.

Keywords: ontology expansion, synthetic dataset, transformer fine-tuning, concept extraction, DOLCE, BERT, taxonomy, LLM, NER

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Authors: Ayaz Ahmad

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In this work, we present the effect of noise on the solution of a partial differential equation (PDE) in three different setting. We shall first consider random initial condition for two nonlinear dispersive PDE the non linear Schrodinger equation and the Kortteweg –de vries equation and analyse their effect on some special solution , the soliton solutions.The second case considered a linear partial differential equation , the wave equation with random initial conditions allow to substantially decrease the computational and data storage costs of an algorithm to solve the inverse problem based on the boundary measurements of the solution of this equation. Finally, the third example considered is that of the linear transport equation with a singular drift term, when we shall show that the addition of a multiplicative noise term forbids the blow up of solutions under a very weak hypothesis for which we have finite time blow up of a solution in the deterministic case. Here we consider the problem of wave propagation, which is modelled by a nonlinear dispersive equation with noisy initial condition .As observed noise can also be introduced directly in the equations.

Keywords: drift term, finite time blow up, inverse problem, soliton solution

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Authors: Niharul Alam

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The development of energy efficient, economic and eco-friendly synthetic protocols for metal nanoparticles (NPs) with tailor-made structural properties and biocompatibility is a highly cherished goal for researchers working in the field of nanoscience and nanotechnology. In this context, green chemistry is highly relevant and the 12 principles of Green Chemistry can be explored to develop such synthetic protocols which are practically implementable. One of the most promising green chemical synthetic methods which can serve the purpose is biogenic synthetic protocol, which utilizes non-toxic multifunctional reactants derived from natural, biological sources ranging from unicellular organisms to higher plants that are often characterized as “medicinal plants”. Over the past few years, a plethora of medicinal plants have been explored as the source of this kind of multifunctional green chemical agents. In this presentation, we focus on the syntheses of stable monometallic Au and Ag NPs and also bimetallic Au/Ag alloy NPs with highly efficient catalytic property using aqueous extract of leaves of Indian Curry leaf plat (Murraya koenigii Spreng.; Fam. Rutaceae) as green multifunctional agents which is extensively used in Indian traditional medicine and cuisine. We have also studied the interaction between the synthesized metal NPs and surface-adsorbed fluorescent moieties, quercetin and quercetin glycoside which are its chemical constituents. This helped us to understand the surface property of the metal NPs synthesized by this plant based biogenic route and to predict a plausible mechanistic pathway which may help in fine-tuning green chemical methods for the controlled synthesis of various metal NPs in future. We observed that simple experimental parameters e.g. pH and temperature of the reaction medium, concentration of multifunctional agent and precursor metal ions play important role in the biogenic synthesis of Au NPs with finely tuned structures.

Keywords: green multifunctional agent, metal nanoparticles, biogenic synthesis

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Authors: Alireza Mohadesi, Ashraf Salmanipour

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A chemically modified glassy carbon electrode for electrocatalytic determination of sulfide was developed using multiwalled carbon nanotubes (MWCNTs) covalently immobilized with 2,6-dichlorophenolindophenol (DPIP). The immobilization of 2,6-dichlorophenolindophenol with MWCNTs was performed with a new synthetic strategy and characterized by UV–visible absorption spectroscopy, Fourier transform infrared spectroscopy and cyclic voltammetry. The cyclic voltammetric response of DPIP grafted onto MWCNTs indicated that it promotes the low potential, sensitive and stable determination of sulfide. The dependence of response currents on the concentration of sulfide was examined and was linear in the range of 10 - 1100 µM. The detection limit of sulfide was 5 µM and RSD for 100 and 500 µM sulfides were 1.8 and 1.3 %. Many interfering species had little or no effect on the determination of sulfide. The procedure was applied to determination of sulfide in waters samples.

Keywords: functionalized carbon nanotubes, sulfide, biological samples, 2, 6-dichlorophenolindophenol

Procedia PDF Downloads 313

Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Atul K. Desai, Hemal J. Shah

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The tall windmill towers are designed as monopole tower or lattice tower. In the present research, a 125-meter high hybrid tower which is a combination of lattice and monopole type is proposed. The response of hybrid tower is compared with conventional monopole tower. The towers were analyzed in finite element method software considering nonlinear seismic time history load. The synthetic seismic time history for different soil is derived using the SeismoARTIF software. From the present research, it is concluded that, in the hybrid tower, we are not getting resonance condition. The base shear is less in hybrid tower compared to monopole tower for different soil conditions.

Keywords: dynamic analysis, hybrid wind mill tower, resonance condition, synthetic time history

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Authors: Osama Abaza, Tanay Datta Chowdhury

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As a driver's speed increases, so do the probability of an incident and likelihood of injury. The presence of equipment, personnel, and a changing landscape in construction zones create greater potential for incident. This is especially concerning in Alaska, where summer construction activity, coinciding with the peak annual traffic volumes, cannot be avoided. In order to reduce vehicular speeding in work zones, and therefore the probability of crash and incident occurrence, variable speed limit (VSL) systems can be implemented in the form of radar speed display trailers since the radar speed display trailers were shown to be effective at reducing vehicular speed in construction zones. Allocation of VSL not only help reduce the 85th percentile speed but also it will predominantly reduce mean speed as well. Total of 2147 incidents along with 385 crashes occurred only in one month around the construction zone in the Alaska which seriously requires proper attention. This research provided a thorough crash analysis to better understand the cause and provide proper countermeasures. Crashes were predominantly recoded as vehicle- object collision and sideswipe type and thus significant amount of crashes fall in the group of no injury to minor injury type in the severity class. But still, 35 major crashes with 7 fatal ones in a one month period require immediate action like the implementation of the VSL system as it proved to be a speed reducer in the construction zone on Alaskan roadways.

Keywords: speed, construction zone, crash, severity

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Authors: Jeremie Coullon, Robert J. Webber

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We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems.

Keywords: Bayesian inverse problems, Markov chain Monte Carlo, infinite-dimensional inverse problems, dimensionality reduction

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Authors: Sangbaek Park, Seungju Lee, Soo-Won Chae

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Unstable shoes are known to strengthen lower extremity muscles and improve gait ability and to change the user’s gait pattern. The change in gait pattern affects human body enormously because the walking is repetitive and steady locomotion in daily life. It is possible to estimate the joint motion including joint moment, force and inertia effect using kinematic and kinetic analysis. However, the change of internal stress at the articular cartilage has not been possible to estimate. The purpose of this research is to evaluate the internal stress of human body during gait with unstable shoes. In this study, FE analysis was combined with motion capture experiment to obtain the boundary condition and loading condition during walking. Motion capture experiments were performed with a participant during walking with normal shoes and with unstable shoes. Inverse kinematics and inverse kinetic analysis was performed with OpenSim. The joint angle and muscle forces were estimated as results of inverse kinematics and kinetics analysis. A detailed finite element (FE) lower extremity model was constructed. The joint coordinate system was added to the FE model and the joint coordinate system was coincided with OpenSim model’s coordinate system. Finally, the joint angles at each phase of gait were used to transform the FE model’s posture according to actual posture from motion capture. The FE model was transformed into the postures of three major phases (1st peak of ground reaction force, mid stance and 2nd peak of ground reaction force). The direction and magnitude of muscle force were estimated by OpenSim and were applied to the FE model’s attachment point of each muscle. Then FE analysis was performed to compare the stress at knee cartilage during gait with normal shoes and unstable shoes.

Keywords: finite element analysis, gait analysis, human model, motion capture

Procedia PDF Downloads 323

Authors: Brayden Mi

Abstract:

Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

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Authors: Vethathirri Ramanujam Srinivasan, S. M. Shiva Nagendra

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Air quality management in urban areas is a serious concern in both developed and developing countries. In this regard, more number of air quality monitoring stations are planned to mitigate air pollution in urban areas. In India, Central Pollution Control Board has set up 574 air quality monitoring stations across the country and proposed to set up another 500 stations in the next few years. The number of monitoring stations for each city has been decided based on population data. The setting up of ambient air quality monitoring stations and their operation and maintenance are highly expensive. Therefore, there is a need to optimize monitoring networks for air quality management. The present paper discusses the various methods such as Indian Standards (IS) method, US EPA method and European Union (EU) method to arrive at the minimum number of air quality monitoring stations. In addition, optimization of rain-gauge method and Inverse Distance Weighted (IDW) method using Geographical Information System (GIS) are also explored in the present work for the design of air quality network in Chennai city. In summary, additionally 18 stations are required for Chennai city, and the potential monitoring locations with their corresponding land use patterns are ranked and identified from the 1km x 1km sized grids.

Keywords: air quality monitoring network, inverse distance weighted method, population based method, spatial variation

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Authors: H. Yousefnia, S. Zolghadri

Abstract:

An early diagnosis of bone metastases is very important for providing a profound decision on a subsequent therapy. A prerequisite for the clinical application of new diagnostic radiopharmaceutical is the measurement of organ radiation exposure dose from biodistribution data in animals. In this study, the dosimetric studies of a novel agent for SPECT-imaging of bone methastases, 111In-(4-{[(bis(phosphonomethyl))carbamoyl]methyl}-7,10-bis(carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid (111In-BPAMD) complex, have been estimated in human organs based on mice data. The radiolabeled complex was prepared with high radiochemical purity at the optimal conditions. Biodistribution studies of the complex were investigated in male Syrian mice at selected times after injection (2, 4, 24 and 48 h). The human absorbed dose estimation of the complex was performed based on mice data by the radiation absorbed dose assessment resource (RADAR) method. 111In-BPAMD complex was prepared with high radiochemical purity >95% (ITLC) and specific activities of 2.85 TBq/mmol. Total body effective absorbed dose for 111In-BPAMD was 0.205 mSv/MBq. This value is comparable to the other 111In clinically used complexes. The results show that the dose to critical organs the complex is well within the acceptable considered range for diagnostic nuclear medicine procedures. Generally, 111In-BPAMD has interesting characteristics and can be considered as a viable agent for SPECT-imaging of the bone metastases in the near future.

Keywords: In-111, BPAMD, absorbed dose, RADAR

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Authors: Jacqueline Michella Anak Nathen

Abstract:

Subgrade soil is an essential component in the design of road structures as it provides lateral support to the pavement. One of the main reasons for the failure of the pavement is the settlement of the subgrade and the high susceptibility to moisture, which leads to a loss of strength of the subgrade. Construction over weak or soft subgrade affects the performance of the pavement and causes instability of the pavement. If the mechanical properties of the subgrade soils are lower than those required, the soil stabilisation method can be an option to improve the soil properties of the weak subgrade. Soil stabilisation is one of the most popular techniques for improving poor subgrade soils, resulting in a significant improvement in the subgrade soil’s tensile strength, shear strength, and bearing capacity. Soil stabilisation encompasses the various methods used to alter the properties of soil to improve its engineering properties. Soil stabilisation can be broadly divided into four types: thermal, electrical, mechanical, and chemical. The most common method of improving the physical and mechanical properties of soils is stabilisation using binders such as cement and lime. However, soil stabilisation with conventional methods using cement and lime has become uneconomical in recent years, so there is a need to look for an alternative, such as fiber. Although not a new technique, adding fiber is a very practical alternative to soil stabilisation. Various types of fibers, such as natural, synthetic, and waste fibers, have been used as stabilising agents to improve the strength and durability of subgrade soils. This review provides a comprehensive comparison of the effectiveness of natural, synthetic, and waste fibers in stabilising subgrade soils.

Keywords: subgrade, soil stabilisation, pavement, fiber, stabiliser

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Authors: Ehsan Mehryaar

Abstract:

The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: Mohamed Lotfy, Ghada Soliman

Abstract:

Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on Convolutional Neural Networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the digit images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd. Our model achieved an accuracy of 98.94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can also be extended to text recognition tasks, such as text classification and sentiment analysis.

Keywords: computer vision, pattern recognition, optical character recognition, deep learning

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Authors: Ryota Kuzuki, Mitsuhiro Kohara, Noboru Kizuki, Satoshi Yoshida, Hidetaka Hirai, Yuta Nezasa

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About fifty years have passed since Japan's gas supply industry became the first in the world to switch from coal and oil to LNG as a city gas feedstock. Since the Japanese government target of net-zero carbon emission in 2050 was announced in October 2020, it has now entered a new era of challenges to commit to the requirement for decarbonization. This paper describes the situation that synthetic methane, produced from renewable energy-derived hydrogen and recycled carbon, is a promising national policy of transition toward net-zero society. In November 2020, the Japan Gas Association announced the 'Carbon Neutral Challenge 2050' as a vision to contribute to the decarbonization of society by converting the city gas supply to carbon neutral. The key technologies is methanation. This paper shows that methanation is a realistic solution to contribute to the decarbonization of the whole country at a lower social cost, utilizing the supply chain that already exists, from LNG plants to burner chips. The challenges during the transition period (2030-2050), as CO2 captured from exhaust of thermal power plants and industrial factories are expected to be used, it is proposed that a system of guarantee of origin (GO) for H2 and CO2 should be established and harmonize international rules for calculating and allocating greenhouse gas emissions in the supply chain, a platform is also needed to manage tracking information on certified environmental values.

Keywords: synthetic methane, recycled carbon fuels, methanation, transition period, environmental value transfer platform

Procedia PDF Downloads 108

Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

Abstract:

The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

Procedia PDF Downloads 140

Authors: Menonjyoti Kalita, Pradip Baishya

Abstract:

In recent times, the world has been facing serious environmental concerns and issues, such as sustainability and cost, due to the overproduction of synthetic materials and their participation in degrading the environment by means of industrial waste and non-biodegradable characteristics. As such, biocomposites come in handy to ease such troubles. Bio-based composites are promising materials for future applications for substituting synthetic packaging materials. The challenge of making packaging materials lighter, safer and cheaper leads to investigating advanced materials with desired properties. Also, awareness of environmental issues forces researchers and manufacturers to spend effort on composite and bio-composite materials fields. This paper explores and tests some nature-friendly materials has been done which can replace low-density plastics. The materials selected included sugarcane bagasse, areca palm, and bamboo leaves. Sugarcane bagasse bamboo leaves and areca palm sheath are the primary material or natural fibre for testing. These products were processed, and the tensile strength of the processed parts was tested in Micro UTM; it was found that areca palm can be used as a good building material in replacement to polypropylene and even could be used in the production of furniture with the help of epoxy resin. And for bamboo leaves, it was found that bamboo and cotton, when blended in a 50:50 ratio, it has great tensile strength. For areca, it was found that areca fibres can be a good substitute for polypropylene, which can be used in building construction as binding material and also other products.

Keywords: biodegradable characteristics, bio-composites, areca palm sheath, polypropylene, micro UTM

Procedia PDF Downloads 90

Authors: Shreekumari Patel, Prabhjot Kaur, Paras Solanki

Abstract:

Moon Mineralogy Mapper (M3), Miniature Radio Frequency (Mini-RF), Kaguya Terrain Camera images, Lunar Orbiter Laser Altimeter (LOLA) digital elevation model (DEM) and Lunar Reconnaissance Orbiter Camera (LROC)- Narrow angle camera (NAC) and Wide angle camera (WAC) images were used to study mineralogy, surface physical properties, and age of the 42 km diameter Kopff crater. M3 indicates the low albedo crater floor to be high-Ca pyroxene dominated associated with floor fracture suggesting the igneous activity of the gabbroic material. Signature of anorthositic material is sampled on the eastern edge as target material is excavated from ~3 km diameter impact crater providing access to the crustal composition. Several occurrences of spinel were detected in northwestern rugged terrain. Our observation can be explained by exposure of spinel by this crater that impacted onto the inner rings of Orientale basin. Spinel was part of the pre-impact target, an intrinsic unit of basin ring. Crater floor was dated by crater counts performed on Kaguya TC images. Nature of surface was studied in detail with LROC NAC and Mini-RF. Freshly exposed surface and boulder or debris seen in LROC NAC images have enhanced radar signal in comparison to mature terrain of Kopff crater. This multidisciplinary analysis of remote sensing data helps to assess lunar surface in detail.

Keywords: crater, mineralogy, moon, radar observations

Procedia PDF Downloads 160

Authors: AlHanoof A. Alharbi, Fatima M. Alamoudi, Razan A. Albrahim, Sarah F. Alharbi, Abdullah M Almuhaideb, Norah A. Almubairik, Abdulrahman Alharby, Naya M. Nagy

Abstract:

Recently, drones received a rapid interest in different industries worldwide due to its powerful impact. However, limitations still exist in this emerging technology, especially privacy violation. These aircrafts consistently threaten the security of entities by entering restricted areas accidentally or deliberately. Therefore, this research project aims to develop drone detection and prevention mechanism to protect the restricted area. Until now, none of the solutions have met the optimal requirements of detection which are cost-effectiveness, high accuracy, long range, convenience, unaffected by noise and generalization. In terms of prevention, the existing methods are focusing on impractical solutions such as catching a drone by a larger drone, training an eagle or a gun. In addition, the practical solutions have limitations, such as the No-Fly Zone and PITBULL jammers. According to our study and analysis of previous related works, none of the solutions includes detection and prevention at the same time. The proposed solution is a combination of detection and prevention methods. To implement the detection system, a passive radar will be used to properly identify the drone against any possible flying objects. As for the prevention, jamming signals and forceful safe landing of the drone integrated together to stop the drone’s operation. We believe that applying this mechanism will limit the drone’s invasion of privacy incidents against highly restricted properties. Consequently, it effectively accelerates drones‘ usages at personal and governmental levels.

Keywords: detection, drone, jamming, prevention, privacy, RF, radar, UAV

Procedia PDF Downloads 211