Search results for: dynamic algorithm

Authors: Mateusz Frydrych

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The construction of diaphragm walls as excavation shoring as well as part of deep foundations is widely used in geotechnical engineering. Today's design challenges lie in the optimal dimensioning of the cross-section, which is demanded by technological considerations. Also in force is the issue of optimization and sustainable use of construction materials, including reduction of carbon footprint, which is currently a relevant challenge for the construction industry. The author presents the concept of an approach to achieving increased efficiency of diaphragm wall excavation shoring by using structural compression technology. The author proposes to implement prestressed tendons in a non-linear manner in the reinforcement cage. As a result bending moment is reduced, which translates into a reduction in the amount of steel needed in the section, a reduction in displacements, and a reduction in the scratching of the casing, including the achievement of better tightness. This task is rarely seen and has not yet been described in a scientific way in the literature. The author has developed a dynamic numerical model that allows the dimensioning of the cross-section of a prestressed shear wall, as well as the study of casing displacements and cross-sectional forces in any defined computational situation. Numerical software from the Sofistik - open source development environment - was used for the study, and models were validated in Plaxis software . This is an interesting idea that allows for optimizing the execution of construction works and reducing the required resources by using fewer materials and saving time. The author presents the possibilities of a prestressed diaphragm wall, among others, using. The example of a diaphragm wall working as a cantilever at the height of two underground floors without additional strutting or stability protection by using ground anchors. This makes the execution of the work more criminal for the contractor and, as a result, cheaper for the investor.

Keywords: prestressed diaphragm wall, Plaxis, Sofistik, innovation, FEM, optimisation

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Authors: Aibek Kukpayev, Dhawal Shah

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Combustion of sour fuels containing high amount of sulfur leads to the formation of sulfur oxides, which adversely harm the environment and has a negative impact on human health. Considering this, several legislations have been imposed to bring down the sulfur content in fuel to less than 10 ppm. In recent years, novel deep eutectic solvents (DESs) have been developed to achieve deep desulfurization, particularly to extract thiophenic compounds from liquid fuels. These novel DESs, considered as analogous to ionic liquids are green, eco-friendly, inexpensive, and sustainable. We herein, using molecular dynamic simulation, analyze the interactions of metal-based DESs with model oil consisting of thiophenic compounds. The DES used consists of polyethylene glycol (PEG-200) as a hydrogen bond donor, choline chloride (ChCl) or tetrabutyl ammonium chloride (TBAC) as a hydrogen bond acceptor, and cobalt chloride (CoCl₂) as metal salt. In particular, the combination of ChCl: PEG-200:CoCl₂ at a ratio 1:2:1 and the combination of TBAC:PEG-200:CoCl₂ at a ratio 1:2:0.25 were simulated, separately, with model oil consisting of octane and thiophenes at 25ᵒC and 1 bar. The results of molecular dynamics simulations were analyzed in terms of interaction energies between different components. The simulations revealed a stronger interaction between DESs/thiophenes as compared with octane/thiophenes, suggestive of an efficient desulfurization process. In addition, our analysis suggests that the choice of hydrogen bond acceptor strongly influences the efficiency of the desulfurization process. Taken together, the results also show the importance of the metal ion, although present in small amount, in the process, and the role of the polymer in desulfurization of the model fuel.

Keywords: deep eutectic solvents, desulfurization, molecular dynamics simulations, thiophenes

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Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: autonomous vehicles, deformable part model, dpm, pedestrian detection, real time

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Authors: W. Hamaidia, T. Zebbiche

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This paper presents the design of axisymmetric supersonic nozzles, in order to accelerate a supersonic flow to the desired Mach number and that having a small weight, in the same time gives a high thrust. The concerned nozzle gives a parallel and uniform flow at the exit section. The nozzle is divided into subsonic and supersonic regions. The supersonic portion is independent to the upstream conditions of the sonic line. The subsonic portion is used to give a sonic flow at the throat. In this case, nozzle gives a uniform and parallel flow at the exit section. It’s named by minimum length Nozzle. The study is done at high temperature, lower than the dissociation threshold of the molecules, in order to improve the aerodynamic performances. Our aim consists of improving the performances both by the increase of exit Mach number and the thrust coefficient and by reduction of the nozzle's mass. The variation of the specific heats with the temperature is considered. The design is made by the Method of Characteristics. The finite differences method with predictor-corrector algorithm is used to make the numerical resolution of the obtained nonlinear algebraic equations. The application is for air. All the obtained results depend on three parameters which are exit Mach number, the stagnation temperature, the chosen mesh in characteristics. A numerical simulation of nozzle through Computational Fluid Dynamics-FASTRAN was done to determine and to confirm the necessary design parameters.

Keywords: flux supersonic flow, axisymmetric minimum length nozzle, high temperature, method of characteristics, calorically imperfect gas, finite difference method, trust coefficient, mass of the nozzle, specific heat at constant pressure, air, error

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Authors: Thi. A. D. Tran, Matthieu Arnold, Dominique Adolphe, Laurence Schcher, Guillaume Reys

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During daily oral health cares, dental surgeons are in contact with numerous potentially infectious germs from patients' saliva and blood. In order to take into account these risks, a product development process has been unrolled to propose to the dental surgeon a personal protection equipment that is suitable with their expectations in terms of images, protection and comfort. After a consumer study, to evaluate how the users wear the garment and their expectations, specifications have been carried out and technical solutions have been developed in order to answer to the maximum of the desiderata. Thermal studies and comfort studies have been performed. The obtained results lead to define the technical solutions concerning the design of the new scrub. Three main functions have been investigated, the ergonomic aspect, the protection and the thermal comfort. In terms of ergonomic aspect, instrumented garments have been worn and pressure measurements have been done. The results highlight that a raglan shape for the sleeves has to be selected for a better dynamic comfort. Moreover, spray tests helped us to localize the potential contamination area and therefore protection devices have been placed on the garment. Concerning the thermal comfort, an I-R study was conducted in consulting room under the real working conditions; the heating zones have been detected. Based on these results, solutions have been proposed and implemented in a new gown. This new gown is currently composed of three different parts; a protective layer placed in the chest area to avoid contamination; a breathable layer placed in the back and in the armpits and a normal PET/Cotton fabric for the rest of the gown. Through the fitting tests conducted in hospital, it was obtained that the new design was highly appreciated. Some points can nevertheless be further improved. A final product will be produced based on necessary improvements.

Keywords: comfort, dentists, garment, thermal

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Authors: Calvin A. Omolo, Rahul S. Kalhapure, Mahantesh Jadhav, Sanjeev Rambharose, Chunderika Mocktar, Thirumala Govender

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Treatment of infections is compromised by limitations of conventional dosage forms and drug resistance. Nanocarrier system is a strategy to overcome these challenges and improve therapy. Thus, the development of novel materials for drug delivery via nanocarriers is essential. The aim of the study was to synthesize a multi-arm polymer (6-mPEPEA) for enhanced activity of vancomycin (VM) against susceptible and resistant Staphylococcus aureus (MRSA). The synthesis steps of the star polymer followed reported procedures. The synthesized 6-mPEPEA was characterized by FTIR, ¹H and ¹³CNMR and MTT assays. VM loaded micelles were prepared from 6-mPEPEA and characterized for size, polydispersity index (PI) and surface charge (ZP) (Dynamic Light Scattering), morphology by TEM, drug loading (UV Spectrophotometry), drug release (dialysis bag), in vitro and in vivo efficacy against sensitive and resistant S. aureus. 6-mPEPEA was synthesized, and its structure was confirmed. MTT assays confirmed its nontoxic nature with a high cell viability (77%-85%). Unimolecular spherical micelles were prepared. Size, PI, and ZP was 52.48 ± 2.6 nm, 0.103 ± 0.047, -7.3 ± 1.3 mV, respectively and drug loading was 62.24 ± 3.8%. There was a 91% drug release from VCM-6-mPEPEA after 72 hours. In vitro antibacterial test revealed that VM-6-mPEPEA had 8 and 16-fold greater activity against S. aureus and MRSA when compared to bare VM. Further investigations using flow cytometry showed that VM-6-mPEPEA had 99.5% killing rate of MRSA at the MIC concentration. In vivo antibacterial activity revealed that treatment with VM-6-mPEPEA had a 190 and a 15-fold reduction in the MRSA load in untreated and VM treated respectively. These findings confirmed the potential of 6-mPEPEA as a promising bio-degradable nanocarrier for antibiotic delivery to improve treatment of bacterial infections.

Keywords: biosafe, MRSA, nanocarrier, resistance, unimolecular-micelles

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Authors: Yebeen Ji, Jimin Yun, Kwonhoo Kim

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Magnesium alloys were limited for industrial applications due to having a limited slip system and high plastic anisotropy. It has been known that specific textures were formed during processing (rolling, etc.), and These textures cause poor formability. To solve these problems, many researchers have studied controlling texture by adding rare-earth elements. However, the high cost limits their use; therefore, alternatives are needed to replace them. Although Pb addition doesn’t directly improve magnesium properties, it has been known to suppress the diffusion of other alloying elements and reduce grain boundary energy. These characteristics are similar to the additions of rare-earth elements, and a similar texture behavior is expected as well. However, there is insufficient research on this. Therefore, this study investigates the behavior of texture and microstructure development after adding Pb to magnesium. This study compared and analyzed AZ61 alloy and Mg-15wt%Pb alloy to determine the effect of adding solute elements. The alloy was hot rolled and annealed to form a single phase and initial texture. Afterward, the specimen was set to contraction and elongate parallel to the rolling surface and the rolling direction and then subjected to high-temperature plane strain compression under the conditions of 723K and 0.05/s. Microstructural analysis and texture measurements were performed by SEM-EBSD. The peak stress in the true strain-stress curve after compression was higher in AZ61, but the shape of the flow curve was similar for both alloys. For both alloys, continuous dynamic recrystallization was confirmed to occur during the compression process. The basal texture developed parallel to the compressed surface, and the pole density was lower in the Mg-15wt%Pb alloy. It is confirmed that this change in behavior is because the orientation distribution of recrystallized grains has a more random orientation compared to the parent grains when Pb is added.

Keywords: Mg, texture, Pb, DRX

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Authors: Gargi Ghoshal, Ashay Jain, Deepika Thakur, U. S. Shivhare, O. P. Katare

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The main objectives of this work were the rheological characterization of dispersions, emulsions at different pH used in the microcapsules preparation and the microcapsules obtain from gum arabic (A), guar gum (G), casein (C) and whey protein isolate (W) to keep β-carotene protected from degradation using the complex coacervation microencapsulation technique (CCM). The evaluation of rheological properties of dispersions, emulsions of different pH and so obtained microencapsules manifest the changes occur in the molecular structure of wall materials during the encapsulation process of β-carotene. These dispersions, emulsions of different pH and formulated microencapsules were subjected to go through various conducted experiments (flow curve test, amplitude sweep, and frequency sweep test) using controlled stress dynamic rheometer. Flow properties were evaluated as a function of apparent viscosity under steady shear rate ranging from 0.1 to 100 s-1. The frequency sweep test was conducted to determine the extent of viscosity and elasticity present in the samples at constant strain under changing angular frequency range from 0.1 to 100 rad/s at 25ºC. The dispersions and emulsion exhibited a shear thinning non-Newtonian behavior whereas microencapsules are considered as shear-thickening respectively. The apparent viscosity for dispersion, emulsions were decreased at low shear rates 20 s-1 and for microencapsules, it decreases up to ~50 s-1 besides these value, it has shown constant pattern. Oscillatory shear experiments showed a predominant viscous liquid behavior up to crossover frequencies of dispersions of C, W, A at 49.47 rad/s, 57.60 rad/s and 21.45 rad/s emulsion sample of AW at pH 5.0 it was 17.85 rad/s and GW microencapsules 61.40 rad/s respectively whereas no such crossover was found in G dispersion, emulsion with C and microencapsules still it showed more viscous behavior. Storage and loss modulus decreases with time also a shift of the crossover towards lower frequencies for A, W and C was observed respectively. However, their microencapsules showed more viscous behavior as compared to samples prior to blending.

Keywords: viscosity, gums, proteins, frequency sweep test, apparent viscosity

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Authors: J. S. Saini, P. P. K. Sandhu

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The multi-hop nature of Wireless Mesh Networks and the hasty progression of throughput demands results in multi- channels and multi-radios structures in mesh networks, but the main problem of co-channels interference reduces the total throughput, specifically in multi-hop networks. Quality of Service mentions a vast collection of networking technologies and techniques that guarantee the ability of a network to make available desired services with predictable results. Quality of Service (QoS) can be directed at a network interface, towards a specific server or router's performance, or in specific applications. Due to interference among various transmissions, the QoS routing in multi-hop wireless networks is formidable task. In case of multi-channel wireless network, since two transmissions using the same channel may interfere with each other. This paper has considered the Destination Sequenced Distance Vector (DSDV) routing protocol to locate the secure and optimised path. The proposed technique also utilizes the Lempel–Ziv–Welch (LZW) based lossless data compression and intra cluster data aggregation to enhance the communication between the source and the destination. The use of clustering has the ability to aggregate the multiple packets and locates a single route using the clusters to improve the intra cluster data aggregation. The use of the LZW based lossless data compression has ability to reduce the data packet size and hence it will consume less energy, thus increasing the network QoS. The MATLAB tool has been used to evaluate the effectiveness of the projected technique. The comparative analysis has shown that the proposed technique outperforms over the existing techniques.

Keywords: WMNS, QOS, flooding, collision avoidance, LZW, congestion control

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Authors: Mihaja Razafimbelo, Guillaume Herve-Secourgeon, Fabrice Gatuingt, Marina Bottoni, Tulio Honorio-De-Faria

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The challenge of the research is to provide engineering with a robust, semi-automatic method for calculating optimal reinforcement for massive structural elements. In the absence of such a digital post-processing tool, design office engineers make intensive use of plate modelling, for which automatic post-processing is available. Plate models in massive areas, on the other hand, produce conservative results. In addition, the theoretical foundations of automatic post-processing tools for reinforcement are those of reinforced concrete beam sections. As long as there is no suitable alternative for automatic post-processing of plates, optimal modelling and a significant improvement of the constructability of massive areas cannot be expected. A method called strut-and-tie is commonly used in civil engineering, but the result itself remains very subjective to the calculation engineer. The tool developed will facilitate the work of supporting the engineers in their choice of structure. The method implemented consists of defining a ground-structure built on the basis of the main constraints resulting from an elastic analysis of the structure and then to start an optimization of this structure according to the fully stressed design method. The first results allow to obtain a coherent return in the first network of connecting struts and ties, compared to the cases encountered in the literature. The evolution of the tool will then make it possible to adapt the obtained latticework in relation to the cracking states resulting from the loads applied during the life of the structure, cyclic or dynamic loads. In addition, with the constructability constraint, a final result of reinforcement with an orthogonal arrangement with a regulated spacing will be implemented in the tool.

Keywords: strut and tie, optimization, reinforcement, massive structure

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Authors: Jonghoon Chun, Insung Kim, Jonghyun Lim, Gun Ro

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We believe that it is possible to provide a solution to reduce patient safety accidents by displaying correct medical records and prescription information through interactive patient identification. Our system is based on the use of smart bands worn by patients and these bands communicate with the hybrid gateways which understand both BLE and Wifi communication protocols. Through the convergence of low-power Bluetooth (BLE) and hybrid gateway technology, which is one of short-range wireless communication technologies, we implement ‘Intelligent Patient Identification and Location Tracking System’ to prevent medical malfunction frequently occurring in medical institutions. Based on big data and IOT technology using MongoDB, smart band (BLE, NFC function) and hybrid gateway, we develop a system to enable two-way communication between medical staff and hospitalized patients as well as to store locational information of the patients in minutes. Based on the precise information provided using big data systems, such as location tracking and movement of in-hospital patients wearing smart bands, our findings include the fact that a patient-specific location tracking algorithm can more efficiently operate HIS (Hospital Information System) and other related systems. Through the system, we can always correctly identify patients using identification tags. In addition, the system automatically determines whether the patient is a scheduled for medical service by the system in use at the medical institution, and displays the appropriateness of the medical treatment and the medical information (medical record and prescription information) on the screen and voice. This work was supported in part by the Korea Technology and Information Promotion Agency for SMEs (TIPA) grant funded by the Korean Small and Medium Business Administration (No. S2410390).

Keywords: BLE, hybrid gateway, patient identification, IoT, safety management, smart band

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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Authors: Richard Draper

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A supercomputer can be constructed from identical building blocks which are small parallel processors connected by a network referred to as the local network. The routers have unused ports which are used to interconnect the building blocks. These connections are referred to as the global network. The address space has a global and a local component (g, l). The conventional way to connect the building blocks is to connect (g, l) to (g’,l). If there are K blocks, this requires K global ports in each router. If a block is of size M, the result is a machine with KM routers having diameter two. To increase the size of the machine to 2K blocks, each router connects to only half of the other blocks. The result is a larger machine but also one with greater diameter. This is a crude description of how the network of the CRAY XC® is designed. In this paper, a family of interconnection networks using routers with K global and M local ports is defined. Coordinates are (c,d, p) and the global connections are (c,d,p)↔(c’,p,d) which swaps p and d. The network is denoted D3(K,M) and is called a Swapped Dragonfly. D3(K,M) has KM2 routers and has diameter three, regardless of the size of K. To produce a network of size KM2 conventionally, diameter would be an increasing function of K. The family of Swapped Dragonflies has other desirable properties: 1) D3(K,M) scales linearly in K and quadratically in M. 2) If L < K, D3(K,M) contains many copies of D3(L,M). 3) If L < M, D3(K,M) contains many copies of D3(K,L). 4) D3(K,M) can perform an all-to-all exchange in KM2+KM time which is only slightly more than the time to do a one-to-all. This paper makes several contributions. It is the first time that a swap has been used to define a linearly scalable family of networks. Structural properties of this new family of networks are thoroughly examined. A synchronizing packet header is introduced. It specifies the path to be followed and it makes it possible to define highly parallel communication algorithm on the network. Among these is an all-to-all exchange in time KM2+KM. To demonstrate the effectiveness of the swap properties of the network of the CRAY XC® and D3(K,16) are compared.

Keywords: all-to-all exchange, CRAY XC®, Dragonfly, interconnection network, packet switching, swapped network, topology

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Authors: Oluwaseun K. Oyebode, Josiah A. Adeyemo

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Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders.

Keywords: computational modelling, evolutionary algorithms, genetic programming, hydrological modelling

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Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

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Authors: Jacob Hatherell, Jason Matthews, Arnaud Marmier

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Anvil Staking is a cold-forming process that is used in the assembly of plain spherical bearings into a rod-end housing. This process ensures that the bearing outer lip conforms to the chamfer in the matching rod end to produce a lightweight mechanical joint with sufficient strength to meet the pushout load requirement of the assembly. Finite Element (FE) analysis is being used extensively to predict the behaviour of metal flow in cold forming processes to support industrial manufacturing and product development. On-going research aims to validate FE models across a wide range of bearing and rod-end geometries by systematically isolating and understanding the uncertainties caused by variations in, material properties, load-dependent friction coefficients and strain rate sensitivity. The improved confidence in these models aims to eliminate the costly and time-consuming process of experimental trials in the introduction of new bearing designs. Previous literature has shown that friction coefficients do not remain constant during cold forming operations, however, the understanding of this phenomenon varies significantly and is rarely implemented in FE models. In this paper, a new approach to evaluate the normal contact pressure versus friction coefficient relationship is outlined using friction calibration charts generated via iterative FE models and ring compression tests. When compared to previous research, this new approach greatly improves the prediction of forming geometry and the forming load during the staking operation. This paper also aims to standardise the FE approach to modelling ring compression test and determining the friction calibration charts.

Keywords: anvil staking, finite element analysis, friction coefficient, spherical plain bearing, ring compression tests

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Erifyli Tsagkari, Stephanie Connelly, Zhaowei Liu, Andrew McBride, William Sloan

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Biofilms play an essential role in treating water in biofiltration systems. The biofilm morphology and function are inextricably linked to the hydrodynamics of flow through a filter, and yet engineers rarely explicitly engineer this interaction. We develop a system that links computer simulation and 3-D printing to optimize and rapidly prototype filter media to optimize biofilm function with the hypothesis that biofilm function is intimately linked to the flow passing through the filter. A computational model that numerically solves the incompressible time-dependent Navier Stokes equations coupled to a model for biofilm growth and function is developed. The model is imbedded in an optimization algorithm that allows the model domain to adapt until criteria on biofilm functioning are met. This is applied to optimize the shape of filter media in a simple flow channel to promote biofilm formation. The computer code links directly to a 3-D printer, and this allows us to prototype the design rapidly. Its validity is tested in flow visualization experiments and by microscopy. As proof of concept, the code was constrained to explore a small range of potential filter media, where the medium acts as an obstacle in the flow that sheds a von Karman vortex street that was found to enhance the deposition of bacteria on surfaces downstream. The flow visualization and microscopy in the 3-D printed realization of the flow channel validated the predictions of the model and hence its potential as a design tool. Overall, it is shown that the combination of our computational model and the 3-D printing can be effectively used as a design tool to prototype filter media to optimize biofilm formation.

Keywords: biofilm, biofilter, computational model, von karman vortices, 3-D printing.

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Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

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Authors: Flavio Cannavo

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Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

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Authors: A. Akbarpour, M. R. Adib Ramezani, M. Zhian, N. Ghorbani Amirabad

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One way to improve the performance of structures against of earthquake is passive control which requires no external power source. In this research, tuned liquid column dampers which are among of systems with the capability to transfer energy between various modes of vibration, are used. For the first time, a liquid column damper for vibration control structure is presented. After modeling this structure in design building software and performing the static and dynamic analysis and obtaining the necessary parameters for the design of tuned liquid column damper, the whole structure will be analyzed in finite elements software. The tuned liquid column dampers are installed on the structure and nonlinear time-history analysis is done in two cases of structures; with and without dampers. Finally the seismic behavior of building in the two cases will be examined. In this study the nonlinear time-history analysis on a twelve-story steel structure equipped with damper subject to records of earthquake including Loma Prieta, Northridge, Imperiall Valley, Pertrolia and Landers was performed. The results of comparing between two cases show that these dampers have reduced lateral displacement and acceleration of levels on average of 10%. Roof displacement and acceleration also reduced respectively 5% and 12%. Due to structural asymmetric in the plan, the maximum displacements of surrounding structures as well as twisting were studied. The results show that the dampers lead to a 10% reduction in the maximum response of structure stories surrounding points. At the same time, placing the dampers, caused to reduce twisting on the floor plan of the structure, Base shear of structure in the different earthquakes also has been reduced on the average of 6%.

Keywords: retrofitting, passive control, tuned liquid column damper, finite element analysis

Procedia PDF Downloads 394

Authors: Mohan Li

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Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions.

Keywords: big data, domain model, urban planning, urban quantitative analysis, machine learning, workflow design

Procedia PDF Downloads 160

Authors: Catherine Mangan, Beth Anderson

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There has been a significant drive to use online or hybrid learning in Higher Education (HE) over recent years. HEs with a virtual presence offer their communities a range of benefits, including the potential for greater inclusivity, diversity, and collaboration; more flexible learning packages; and more engaging, dynamic content. Institutions can also experience significant challenges when seeking to extend learning spaces in this way, as can learners themselves. For example, staff members’ and learners’ digital literacy varies (as do their perceptions of technologies in use), and there can be confusion about optimal approaches to implementation. Furthermore, the speed with which HE institutions have needed to shift to fully online or hybrid models, owing to the COVID19 pandemic, has highlighted the significant barriers to successful implementation. HE environments have been shown to predict a range of organisational, academic, and experiential outcomes, both positive and negative. Much research has focused on the social aspect of virtual platforms, as well as the nature and effectiveness of the technologies themselves. There remains, however, a relative paucity of synthesised knowledge on the psychology of learners’ relationships with their institutions; specifically, how individual difference and interpersonal factors predict students’ ability and willingness to engage with novel virtual learning spaces. Accordingly, extending learning spaces remains challenging for institutions, and wholly remote courses, in particular, can experience high attrition rates. Focusing on the last five years, this pragmatic review summarises evidence from the psychological and pedagogical literature. In particular, the review highlights the importance of addressing the psychological and relational complexities of students’ shift from offline to online engagement. In doing so, it identifies considerations for HE institutions looking to deliver in this way.

Keywords: higher education, individual differences, interpersonal relationships, online learning, virtual environment

Procedia PDF Downloads 157

Authors: L. Gyansah, Yanfang Shen, Jiqiang Wang, Tianying Xiong

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SiCₚ/ pure Al composites with different SiC fractions (20 wt %, 30 wt %, and 40 wt %) were precisely cold sprayed, followed by hot axial-compression tests at deformation temperatures of 473 K to 673 K, leading to failure of specimens through routine crack propagation in their multiphase. The plastic deformation behaviour with respect to the SiCₚ contents and the deformation temperatures were studied at strain rate 1s-1.As-sprayed and post-failure specimens were analyzed by X-ray computed tomography (XCT), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Quasi-static thermomechanical testing results revealed that compressive strength (UTS = 228 MPa and 30.4 %) was the highest in the composites that was thermomechanically compressed at 473 K compared to those of the as-sprayed, while the as-sprayed exhibited a compressive strength of 182.8 MPa related to the increment in SiC fraction. Strength—plasticity synergy was promoted by dynamic recrystallization (DRX) through strengthening and refinement of the grains. The DRX degree depends relevantly on retainment of the uniformly ultrafine SiCₚ particulates, the pinning effects of the interfaces promoted by the ultrafine grain structures (UFG), and the higher deformation temperature. Reconstructed X-ray computed tomography data revealed different crack propagation mechanisms. A single-plane shear crack with multi-laminates fracture morphology yields relatively through the as-sprayed and as-deformed at 473 K deposits, while a multiphase plane shear cracks preeminently existed in high temperature deformed deposits resulting in multiphase-interface delaminations. Three pertinent strengthening mechanisms, videlicet, SiCp dispersed strengthening, refined grain strengthening, and dislocation strengthening, existed in the gradient microstructure, and their detailed contributions to the thermomechanical properties were discussed.

Keywords: cold spraying, hot deformation, deformation temperature, thermomechancal properties, SiC/Al composite

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Authors: Wahab Ali Shah, Junjia He

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Transferring electrical power without any wiring has been a dream since late 19^th century. There were some advances in this area as to know more about microwave systems. However, this subject has recently become very attractive due to their practiScal systems. There are low power applications such as charging the batteries of contactless tooth brushes or implanted devices, and higher power applications such as charging the batteries of electrical automobiles or buses. In the first group of applications operating frequencies are in microwave range while the frequency is lower in high power applications. In the latter, the concept is also called inductive power transfer. The aim of the paper is to have an overview of the inductive power transfer for electrical vehicles with a special concentration on coil design and power converter simulation for static charging. Coil design is very important for an efficient and safe power transfer. Coil design is one of the most critical tasks. Power converters are used in both side of the system. The converter on the primary side is used to generate a high frequency voltage to excite the primary coil. The purpose of the converter in the secondary is to rectify the voltage transferred from the primary to charge the battery. In this paper, an inductive power transfer system is studied. Inductive power transfer is a promising technology with several possible applications. Operation principles of these systems are explained, and components of the system are described. Finally, a single phase 2 kW system was simulated and results were presented. The work presented in this paper is just an introduction to the concept. A reformed compensation network based on traditional inductor-capacitor-inductor (LCL) topology is proposed to realize robust reaction to large coupling variation that is common in dynamic wireless charging application. In the future, this type compensation should be studied. Also, comparison of different compensation topologies should be done for the same power level.

Keywords: coil design, contactless charging, electrical automobiles, inductive power transfer, operating frequency

Procedia PDF Downloads 233

Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

Procedia PDF Downloads 138

Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

Procedia PDF Downloads 59

Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

Procedia PDF Downloads 162

Authors: Mahmoud Abdel Hameed Shahin

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Job demands that are incompatible with an employee's skills, resources, or needs cause unpleasant emotional and physical reactions known as job stress. Nurses offer care in hospital emergency rooms all around the world, and since they operate in such a dynamic and unpredictable setting, they are constantly under pressure. It has been discovered that job stress has harmful impacts on nurses' health as well as their capacity to handle the demands of their jobs. The purpose of this study was to evaluate the level of job stress experienced by the emergency department nurses at King Fahad Specialist Hospital in Buraidah City, Saudi Arabia. In October 2021, a cross-sectional descriptive study was conducted. 80 nurses were conveniently selected for the study, the bulk of them worked at King Fahad Specialist Hospital's emergency department. An electronic questionnaire with a sociodemographic data sheet and a job stress scale was given to the participating nurses after ethical approval was received from the Ministry of Health's representative bodies. Using SPSS Version 26, both descriptive and inferential statistics were employed to analyze and tabulate the acquired data. According to the findings, the factors that contributed to the most job stress in the clinical setting were having an excessive amount of work to do and working under arbitrary deadlines, whereas the factors that contributed to the least stress were receiving the proper recognition or rewards for good work. In the emergency room of King Fahad Specialist Hospital, nurses had a moderate level of stress (M=3.32 ± 0.567/5). Based on their experience, emergency nurses' levels of job stress varied greatly, with nurses with less than a year of experience notably experiencing the lowest levels of job stress. The amount of job stress did not differ significantly based on the emergency nurses' age, nationality, gender, marital status, position, or level of education. The causes and impact of stress on emergency nurses should be identified and alleviated by hospitals through the implementation of interventional programs.

Keywords: emergency nurses, job pressure, Qassim, Saudi Arabia, job stress

Procedia PDF Downloads 162