Search results for: strain sensors

Authors: Hatem Shreha

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Aromatase inhibitors administered before sexual differentiation of the gonads can induce sex reversal in female chickens (phenotypic male). To analyze the process of sex reversal, we have followed for several months the changes induced by Fadrozole, a nonsteroidal aromatase inhibitor on the morphology of female sex-reversed and female sex-reversed supplemented with L-tyrosine which was previously shown to stimulate the release of Gn Rh. Fadrozole (1mg/egg) was injected into eggs on day four of incubation, phenotypic males and phenotypic males treated with L-tyrosine and males hatched from eggs injected Fadrozole were sacrificed by slaughtering at 16 weeks old and the remaining chicks were sacrificed at 28 weeks old. Both sexes from control chickens were sacrificed at the same age (16 &28 weeks). Hatchability, behavior, body weight, shank length, comb weight, testes weight, blood cells count and wattle weight of sex reversal were tested at 16 and 28 weeks. The results showed that body weight, comb weight, wattles weight and shank length of sex-reversed females were significantly different from control female. Behavior of phenotypic males and phenotypic males fed on L- tyrosine showed aggressive sexual behavior like that of control males and absence of laying behavior. In conclusion our results confirm that Fedrazole injection in eggs before sex differentiation produce a male behavior and morphological index of male in female chicken.

Keywords: sex reversal, fadrozole, phenotypic male, L- tyrosine

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Authors: Mozhgan Chaichi, Farhad Sharif, Saeede Mazinani

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Polymer nanocomposites are a new class of materials for fabricating future multifunctional and lightweight structures. To obtain good mechanical, thermal and electrical properties, it is essential to achieve uniform dispersion of nanoparticles in polymer matrix. Alignment of nanoparticles in matrix can enhance mechanical, thermal, electrical and barrier properties of nanocomposites in oriented direction. Fe3O4 nanoparticles have generated huge activity in many areas of science and engineering due to its magnetic properties. Magnetic nanoparticles have been investigated for a wide range of applications in sensors, magnetic energy storage, environmental remediation, heterogeneous catalysts and drug delivery. The magnetic response from the Fe3O4 nanoparticles can facilitate with the alignment of nanofillers in a polymer matrix under magnetic field, aiming at fabricating composites with directional properties and functions. Here we report oriented nanocomposites based on Fe3O4 nanoparticles and poly (vinyl alcohol) (PVA), which prepared via a facile aqueous solution by applying a low external magnetic field (750 G). A significant enhancement of mechanical properties, and especially toughness of nanofilms, of oriented PVA/ Fe3O4 nanocomposites is obtained at low nanoparticles loading. Orientation of nanoparticles can align polymer chains and enhance mechanical properties. For example, orientation of 0.1 wt. % Fe3O4 nanoparticles increase 31% toughness and 23% modulus of oriented nanocomposite in compare of pure films, which indicate good dispersion of nanoparticles and efficient load transfer between nanoparticles and matrix.

Keywords: magnetic nanoparticles, nanocomposites, toughness, orientation

Procedia PDF Downloads 329

Authors: Rouag N., Khalifa M. W., Bencheikh A., Abed H.

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The behavior study of forty-two varieties and genotypes of chickpeas regarding root wilt disease induced by Fusarium oxysporum under the natural conditions of infection was conducted at the ITGC experimental station in Sétif. The infected plants of the different chickpea genotypes have shown multiple symptoms in the field caused by the local strain of Fusarium oxysporum f.sp.cecris belonging to race II of the pathogen. These symptoms ranged from lateral or partial wilting of some ramifications to total desiccation of the plant, sometimes combined with the very slow growth of symptomatic plants. The results of the search for sources of resistance to Fusarium wilt of chickpeas in the 42 genotypes tested revealed that in terms of infection rate, the presence of 7 groups and no genotype showed absolute resistance. While in terms of severity, the results revealed the presence of three homogeneous groups. The first group formed by the most resistant genotypes, in this case, Flip10-368C; Flip11-77C; Flip11-186C; Flip11-124C; Flip11-142C, Flip11-152C; Flip11-69C; Ghab 05; Flip11-159C; Flip11-90C; Flip10-357C and Flip11-37C while the second group is the FLIP genotype 10-382C which was found to be the most sensitive for the natural infection test. Thus, the genotypes of Cicer arietinum L., which have shown significant levels of resistance to Fusarium wilt, can be integrated into breeding and improvement programs.

Keywords: chickpea, Cicer arietinum, Fusarium oxysporum, genotype resistance

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Authors: Donatella Cimini, Sergio D’ambrosio, Saba Sadiq, Chiara Schiraldi

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Chondroitin sulfate (CS), as well as modified CS, and unsulfated chondroitin, are largely applied in research today. CS is a linear glycosaminoglycan normally present in cartilage-rich tissues and bones in the form of proteoglycans decorated with sulfate groups in different positions. CS is used as an effective non-pharmacological alternative for the treatment of osteoarthritis, and other potential applications in the biomedical field are being investigated. Some bacteria, such as E. coli K4, produce a polysaccharide that is a precursor of CS (unsulfated chondroitin). This work focused on the construction of integrative E. coli K4 recombinant strains overexpressing genes (kfoA, kfoF, pgm and galU in different combinations) involved in the biosynthesis of the nucleotide sugars necessary for polysaccharide synthesis. Strain growth and polymer production were evaluated using renewable waste materials as substrates in shake flasks and small-scale batch fermentation processes. Results demonstrated the potential to replace pure sugars with cheaper medium components to establish environmentally sustainable and cost-effective production routes for potential industrial development. In fact, although excellent fermentation results have been described so far by employing strains that naturally produce chondroitin-like polysaccharides on semi-defined media, there is still the need to reduce manufacturing costs by providing a cost-effective biotechnological alternative to currently used animal-based extraction procedures.

Keywords: E. coli K4, chondroitin, microbial cell factories, glycosaminoglycans, renewable resources

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Authors: Ayan Giri, Lukaranjan Phukan, Shantanu Karmakar

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Structure and tectonics play a vital role in ore genesis and deposition. The existence of a swelling structure below the current level of a mine leads to the discovery of ores below some permeant developments of the mine. The discovery and the extraction of the ore body are very critical to sustain the business requirement of the mine. The challenge was to extract the ore without hampering the global stability of the mine. In order to do so, different mining options were considered and analysed by numerical modelling in FLAC3d software. The constitutive model prepared for this simulation is the improved unified constitutive model, which can better and more accurately predict the stress-strain relationships in a continuum model. The IUCM employs the Hoek-Brown criterion to determine the instantaneous Mohr-Coulomb parameters cohesion (c) and friction (ɸ) at each level of confining stress. The extra swelled part can be dimensioned as north-south strike width 50m, east-west strike width 50m. On the north side, already a stope (P1) is excavated of the dimension of 25m NS width. The different options considered were (a) Open stoping of extraction of southern part (P0) of 50m to the full extent, (b) Extraction of the southern part of 25m, then filling of both the primaries and extraction of secondary (S0) 25m in between. (c) Extraction of the southern part (P0) completely, preceded by backfill and modify the design of the secondary (S0) for the overall stability of the permanent excavation above the stoping.

Keywords: extraction, IUCM, FLAC 3D, stoping, tectonics

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Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

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Authors: Eleni Maria Pavlopoulou, Vasiliki N. Georgiannou, Filippos C. Chortis

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The response of M31 sand stabilized with colloidal silica (CS) aqueous gel is investigated in the laboratory. CS is introduced in the water regime, forming a hydrosol. The low viscosity hydrosol thickens in a controllable manner to form a stable, non-toxic gel; the gel fills the pore space, retains the pore water, and supports the grain structure. The role of colloidal silica on subsequent sand behavior is examined with the aid of direct shear, triaxial, and normal compression tests. Under the examined loading modes, while the strength of the treated sand is enhanced, its stiffness may reduce, and its compressibility increase. However, in most geotechnical problems, the loading conditions are complex, involving changes in both stress magnitude and direction. Rotation of principal stresses (σ1, σ2, σ3) in varying amounts expressed as angle α, (from α=0° to 90°) in concurrence with increasing shear stress loading is commonly encountered in soil structures such as foundations, embankments, underwater slopes. To assess the influence of anisotropy on the response of sands before and after their stabilization, hollow cylinder tests were performed. The behavior of stabilized sand is compared with the characteristic sand behavior, i.e., a reduction in peak stress ratio associated with a softer stress-strain response with the increasing angle a. The influence of the magnitude of the intermediate principal stress (σ2) on the mechanical response of treated and untreated sand is also examined.

Keywords: anisotropy, colloidal silica, laboratory tests, sands, soil stabilization

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Authors: Zachary Smith, Riette Bloomfield, Taylor Dukate, Joshua Halterman, Noah Phillips

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College students have stressful lives, classes, work, and home life; it all adds up to anxiety and stress. Most students can manage the stress, but some can’t and need help. Mental health issues are on the rise among college-age students, which could lead to other health issues, depression, or even suicidal thoughts. There needs to be an outlet for these students, and one suggestion is participating in sports or exercise/recreation activities. “Strong body, strong mind” is a concept that has been researched for many decades now. While that has been preached, depression and anxiety have still been at an all-time high in college students within the last five years. College students are expected to stay on top of their academic coursework, obtain and keep relationships, adjust to living independently, and economic strain. As p oor mental health becomes inherent, struggles academically, dropping out of school, becoming involved in immoral situations, or as far as committing suicide, can be seen shortly after. This research proposal examines the positive impact of sports on students' mental health in post-secondary education programs. The study aims to investigate how participation in college sports can alleviate stress, anxiety, and depression, improve mood and focus, and contribute to better academic performance. With the increasing prevalence of mental health issues among college students and the growing emphasis on mental health awareness, this research is significant for understanding and managing collegiate sports programs. Overall, sports help with mental and physical health for all ages.

Keywords: mental health, sports, college students, recreation programs

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Authors: Sang-Woo Kim, Young-Seon Lee

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An important concern in metal forming, even at elevated temperatures, is whether a desired deformation can be accomplished without any failure of the material. A detailed understanding of the critical condition for crack initiation provides not only the workability limit of a material but also a guide-line for process design. This paper describes the utilization of ductile fracture criteria in conjunction with the finite element method (FEM) for predicting the onset of fracture in warm metal working processes of magnesium alloy sheets. Critical damage values for various ductile fracture criteria were determined from uniaxial tensile tests and were expressed as the function of strain rate and temperature. In order to find the best criterion for failure prediction, Erichsen cupping tests under isothermal conditions and FE simulations combined with ductile fracture criteria were carried out. Based on the plastic deformation histories obtained from the FE analyses of the Erichsen cupping tests and the critical damage value curves, the initiation time and location of fracture were predicted under a bi-axial tensile condition. The results were compared with experimental results and the best criterion was recommended. In addition, the proposed methodology was used to predict the onset of fracture in non-isothermal deep drawing processes using an irregular shaped blank, and the results were verified experimentally.

Keywords: magnesium, AZ31 alloy, ductile fracture, FEM, sheet forming, Erichsen cupping test

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Authors: Armanul Hoque Shaon, Md Baizid Mahmud, Askander Nobi, Md. Raju Ahmed, Md. Jiabul Hoque

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In the Internet of Things (IoT), devices are linked to the internet through a wireless network, allowing them to collect and transmit data without the need for a human operator. Agriculture relies heavily on wireless sensors, which are a vital component of the Internet of Things (IoT). This kind of wireless sensor network monitors physical or environmental variables like temperatures, sound, vibration, pressure, or motion without relying on a central location or sink and collaboratively passes its data across the network to be analyzed. As the primary source of plant nutrients, the soil is critical to the agricultural industry's continued growth. We're excited about the prospect of developing an Internet of Things (IoT) solution. To arrange the network, the sink node collects groundwater levels and sends them to the Gateway, which centralizes the data and forwards it to the sensor nodes. The sink node gathers soil moisture data, transmits the mean to the Gateways, and then forwards it to the website for dissemination. The web server is in charge of storing and presenting the moisture in the soil data to the web application's users. Soil characteristics may be collected using a networked method that we developed to improve rice production. Paddy land is running out as the population of our nation grows. The success of this project will be dependent on the appropriate use of the existing land base.

Keywords: IoT based agriculture monitoring, intelligent irrigation, communicating network, rice production

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Authors: Debadi Chakraborty, John E. Sader

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Nanomechanical devices have emerged as a versatile platform for a host of applications due to their extreme sensitivity to environmental conditions. For example, mass measurements with sensitivity at the atomic level have recently been demonstrated. Ultrafast laser spectroscopy coherently excite the vibrational modes of metal nanoparticles and permits precise measurement of the vibration characteristics as a function of nanoparticle shape, size and surrounding environment. This study reports that the vibration of metal nanoparticles in simple liquids, like water and glycerol are not described by conventional fluid mechanics, i.e., Navier Stokes equations. The intrinsic molecular relaxation processes in the surrounding liquid are found to have a profound effect on the fluid-structure interaction of mechanical devices at nanometre scales. Theoretical models have been developed based on the non-Newtonian viscoelastic fluid-structure interaction theory to investigate the vibration of nanoparticles immersed in simple fluids. The utility of this theoretical framework is demonstrated by comparison to measurements on single nanowires and ensembles of metal rods. This study provides a rigorous foundation for the use of metal nanoparticles as ultrasensitive mechanical sensors in fluid and opens a new paradigm for understanding extremely high frequency fluid mechanics, nanoscale sensing technologies, and biophysical processes.

Keywords: fluid-structure interaction, nanoparticle vibration, ultrafast laser spectroscopy, viscoelastic damping

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Authors: Aderet M. Pantierer, Shmuel Pantierer, Raphael Cordina, Yougashwar Budhoo

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Stress concentration occurs when there is an abrupt change in geometry, a mechanical part under loading. These changes in geometry can include holes, notches, or cracks within the component. The modifications create larger stress within the part. This maximum stress is difficult to determine, as it is directly at the point of the minimum area. Strain gauges have yet to be developed to analyze stresses at such minute areas. Therefore, a stress concentration factor must be utilized. The stress concentration factor is a dimensionless parameter calculated solely on the geometry of a part. The factor is multiplied by the nominal, or average, stress of the component, which can be found analytically or experimentally. Stress concentration graphs exist for common loading conditions and geometrical configurations to aid in the determination of the maximum stress a part can withstand. These graphs were developed from historical data yielded from experimentation. This project seeks to verify a stress concentration graph for combined loading conditions. The aforementioned graph was developed using CATIA Finite Element Analysis software. The results of this analysis will be validated through further testing. The 3D modeled parts will be subjected to further finite element analysis using Patran-Nastran software. The finite element models will then be verified by testing physical specimen using a tensile testing machine. Once the data is validated, the unique stress concentration graph will be submitted for publication so it can aid engineers in future projects.

Keywords: stress concentration, finite element analysis, finite element models, combined loading

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Authors: Kavitha A.S., L.Govindaraju

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Reinforced earth walls offer excellent solution to many problems associated with earth retaining structures especially under seismic conditions. Use of cohesive soils as backfill material reduces the cost of reinforced soil walls if proper drainage measures are taken. This paper presents a numerical study on the application of a new technique called sandwich technique in reinforced earth walls. In this technique, a thin layer of granular soil is placed above and below the reinforcement layer to initiate interface friction and the remaining portion of the backfill is filled up using the existing insitu cohesive soil. A 6 m high reinforced earth wall has been analysed as a two-dimensional plane strain finite element model. Three types of reinforcing elements such as geotextile, geogrid and metallic strips were used. The horizontal wall displacements and the tensile loads in the reinforcement were used as the criteria to evaluate the results at the end of construction and dynamic excitation phases. Also to verify the effectiveness of sandwich layer on the performance of the wall, the thickness of sand fill surrounding the reinforcement was varied. At the end of construction stage it is found that the wall with sandwich type backfill yielded lower displacements when compared to the wall with cohesive soil as backfill. Also with sandwich backfill, the reinforcement loads reduced substantially when compared to the wall with cohesive soil as backfill. Further, it is found that sandwich technique as backfill and geogrid as reinforcement is a good combination to reduce the deformations of geosynthetic reinforced walls during seismic loading.

Keywords: geogrid, geotextile, reinforced earth, sandwich technique

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

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Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

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The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

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Authors: Feicheng Liu, Weiming Liao, Jianjing Zhang

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A pile-supported embankment with geosynthetic-reinforced mat (PSGR embankment) has been considered as an effective solution to reduce the total and differential settlement of the embankment constructed over soft soil. In this paper, a new simplified method proposed firstly incorporates the load transfer between piles and surrounding soil and the settlement of pile, and also considers arching effect in embankment fill, membrane effect of geosynthetic reinforcement, and subsoil resistance, to evaluate the behavior of PSGR embankment. Subsoil settlement is assumed to consist of two parts:(1) the settlement of subsoil surface between piles equivalent to that of pile caps assuming the geosynthetic reinforcement without deformation yet; (2) the subsoil subsiding along with the geosynthetic deforming, and the deflected geosynthetic being considered as centenary. The force equilibrium, including loads acting on the upper surface of geosynthetic, subsoil resistance, as well as the stress-strain relationship of the geosynthetic reinforcement at the edge of pile cap, is established, thus the expression of subsoil resistance is deduced, and subsequently the tension of geosynthetic and stress concentration ratio between piles can be calculated. The proposed method is validated through observed data from three field tests and also compared with other eight analytical solutions available in the literature. In addition, a sensitive analysis is provided to demonstrate the influence of with/without considering pile-soil interaction for evaluating the performance of PSGR embankment.

Keywords: pile-supported embankment, geosynthetic, analytical solution, soil arching effect, the settlement of pile, sensitive analysis

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Authors: Lingling Shui, Shuting Xie

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As alternatives to electronic devices, optically active structures from responsive nanomaterials offer great opportunity buildup smart functional sensors. Hereby, we report on droplet microfluidics enabled construction and application of photonic microcapsules (PMCs) for colorimetric temperature microsensors, enabling miniaturization for injectable local micro-area sensing and integration for large-area sensing. Monodispersed PMCs are produced by in-situ photopolymerization of hydrogel shells of cholesteric liquid crystal (CLC)-in-water-in-oil double emulsion droplets prepared using microfluidic devices, with controllable physical structures and chemical compositions. Constructed PMCs exhibit thermal responsive structural color according to the selective Bragg reflection of CLC’s periodical helical structures within the microdroplet’s spherical confinement. Constructed PMCs with tunable size and composition have been successfully applied for monitoring the living cell extracellular temperature via co-incubation with cell suspension, and for detecting human body temperature via a flexible device from assembled PMCs. These PMCs could be flexibly applied in either micro-environment or large-area surface, enabling wide applications for precision temperature monitoring biological activities (e.g. cells or organs), optoelectronic devices working conditions (e.g. temperature indicators under extreme conditions), and etc.

Keywords: droplet, microfluidics, assembly, soft materials, microsensor

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Authors: Bulcha Belay Etana

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Research shows that several challenges are to be resolved for textile sensors and wearable smart textiles systems to make it accurate and reproducible minimizing variability issues when tested. To achieve this, we developed stimulating embroidery electrode with three different filling textiles such as 3Dknit, microfiber, and nonwoven fabric, and tested with FTT for high recoverability on compression. Hence The impedance characteristics of wetted electrodes were caried out after 1hr of wetting under normal environmental conditions. The wetted 3D knit (W-3D knit), Wetted nonwoven (W-nonwoven), and wetted microfiber (W-microfiber) developed using Satin stitch performed better than a dry standard stitch or dry Satin stitch electrodes. Its performance was almost the same as that of the gel electrode (Ag/AgCl) as shown by the impedance result in figure 2 .The impedance characteristics of Dry and wetted 3D knit based Embroidered electrodes are better than that of the microfiber, and nonwoven filling textile. This is due to the fact that 3D knit fabric has high recoverability on compression to retain electrolyte gel than microfiber, and nonwoven. However,The non-woven fabric held the electrolyte for longer time without releasing it to the skin when needed, thus making its impedance characteristics poor as observed from the results. Whereas the dry Satin stitch performs better than the standard stitch based developed electrode. The inter electrode distance of all types of the electrode was 25mm, with the area of the electrode being 20mm by 20mm. Detail evaluation and further analysis is in progress for EMG monitoring application

Keywords: impedance, moisture retention, 3D knit fabric, microfiber, nonwoven

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Walid Abdallah, Noureddine Boudriga

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Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

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Authors: Kamélia Bouderbala, Hichem Nouira, Etienne Videcoq, Manuel Girault, Daniel Petit

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Thermal drifts caused by the power dissipated by the mechanical guiding systems constitute the main limit to enhance the accuracy of an ultra-high precision cylindricity measuring machine. For this reason, a high precision compact prototype has been designed to simulate the behaviour of the instrument. It ensures in situ calibration of four capacitive displacement probes by comparison with four laser interferometers. The set-up includes three heating wires for simulating the powers dissipated by the mechanical guiding systems, four additional heating wires located between each laser interferometer head and its respective holder, 19 Platinum resistance thermometers (Pt100) to observe the temperature evolution inside the set-up and four Pt100 sensors to monitor the ambient temperature. Both a Reduced Model (RM), based on the Modal Identification Method (MIM) was developed and optimized by comparison with the experimental results. Thereafter, time dependent tests were performed under several conditions to measure the temperature variation at 19 fixed positions in the system and compared to the calculated RM results. The RM results show good agreement with experiment and reproduce as well the temperature variations, revealing the importance of the RM proposed for the evaluation of the thermal behaviour of the system.

Keywords: modal identification method (MIM), thermal behavior and drift, dimensional metrology, measurement

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Authors: Yasaman Sanayei, Alireza Bahiraie

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This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

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Authors: Sally Hosny, Mona G. Ibrahim, N. K. Hassan

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Fiber reinforced polymers (FRP) is one of the most environmentally method for strengthening and retrofitting steel structure buildings. The behaviour of flexural strengthened steel I-beams using FRP was investigated. The finite element (FE) models were developed using ANSYS® as verification cases to simulate the experimental behaviour of using FRP strips to flexure strengthen steel I-beam. Two experimental studies were selected for verification; first examined the effect of different thicknesses and modulus of elasticity while the second studied the effect of applying different carbon fiber reinforced polymers (CFRP) bond lengths. The proposed FE models were in good agreement with the experimental results in terms of failure modes, load bearing capacities and strain distribution on CFRP strips. The verified FE models can be utilized to conduct a parametric study where various widths (40, 50, 60, 70 and 80 mm), thickness (1.2, 2 and 4 mm) and lengths (1500, 1700 and 1800 mm) of CFRP were analyzed. The results presented clearly revealed that the load bearing capacity was significantly increased (+7%) when the width and thickness were increased. However, load bearing capacity was slightly affected using longer CFRP strips. Moreover, applying another glass fiber reinforced polymers (GFRP) of 1500 mm in length, 50 mm in width and thicknesses of 1.2, 2 and 4 mm were investigated. Load bearing capacity of strengthened I-beams using GFRP is less than CFRP by average 8%. Statistical analysis has been conducted using Minitab®.

Keywords: FRP, strengthened steel I-beams, flexural, FEM, ANSYS

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Authors: J. Subbalakshmi, G. Dhruvasamhith, S. M. Hussain

Abstract:

Polyaniline (PANI) is one of the most extensively studied material among the conducting polymers due to its simple synthesis by chemical and electrochemical routes. PANIs have advantages of chemical stability and high conductivity making their commercial applications quite attractive. However, to our knowledge, very little work has been reported on the tensile strength properties of templated PANIs processed with polyvinyl alcohol and also, detailed study has not been carried out. We have investigated the effect of small molecule and polymers as templates on PANI. Stable aqueous colloidal suspensions of trisodium citrate (TSC), poly(ethylenedioxythiophene)-polystyrene sulfonate (PEDOT-PSS), and polyethylene glycol (PEG) templated PANIs were prepared through chemical synthesis, processed with polyvinyl alcohol (PVA) and were fabricated into films by solution casting. Absorption and infra-red spectra were studied to gain insight into the possible molecular interactions. Surface morphology was studied through scanning electron microscope and optical microscope. Interestingly, tensile testing studies revealed least strain for pure PVA when compared to the blends of templated PANI. Furthermore, among the blends, TSC templated PANI possessed maximum elasticity. The ultimate tensile strength for PVA processed, PEG-templated PANI was found to be five times more than other blends considered in this study. We establish structure–property correlation with morphology, spectral characterization and tensile testing studies.

Keywords: surface morphology, processed films, polyvinyl alcohol, templated polyanilines, tensile testing

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Authors: Jisun Lee, Jay Hyoun Kwon

Abstract:

As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

Procedia PDF Downloads 349

Authors: Shivani Verma, Amit Agrawal, Pankaj Kumar Meena, Ashish B. Deoghare

Abstract:

Over the decade robotics has been a major area of interest among the researchers and scientists in reducing human efforts. The need for robots to replace human work in different dangerous fields such as underground mining, nuclear power station and war against terrorist attack has gained huge attention. Most of the robot design is based on human structure popularly known as humanoid robots. However, the problems encountered in humanoid robots includes low speed of movement, misbalancing in structure, poor load carrying capacity, etc. The simplification and adaptation of the fundamental design principles seen in animals have led to the creation of bio-inspired robots. But the major challenges observed in naturally inspired robot include complexity in structure, several degrees of freedom and energy storage problem. The present work focuses on design and fabrication of a bionic quadruped walking robot which is based on different joint of quadruped mammals like a dog, cheetah, etc. The design focuses on the structure of the robot body which consists of four legs having three degrees of freedom per leg and the electronics system involved in it. The robot is built using readily available plastics and metals. The proposed robot is simple in construction and is able to move through uneven terrain, detect and locate obstacles and take images while carrying additional loads which may include hardware and sensors. The robot will find possible application in the artificial intelligence sector.

Keywords: artificial intelligence, bionic, quadruped robot, degree of freedom

Procedia PDF Downloads 215

Authors: Huawei Bai, Jianguo Yao, Fellow, IEEE

Abstract:

Atrial fibrillation (AF) is the most common form of heart arrhythmia and is closely associated with mortality and morbidity in heart failure, stroke, and coronary artery disease. The development of single spot optical sensors enables widespread photoplethysmography (PPG) screening, especially for AF, since it represents a more convenient and noninvasive approach. To our knowledge, most existing studies based on public and unbalanced datasets can barely handle the multiple noises sources in the real world and, also, lack interpretability. In this paper, we construct a large- scale PPG dataset using measurements collected from PPG wrist- watch devices worn by volunteers and propose an attention-based two-stream heterogeneous neural network (TSHNN). The first stream is a hybrid neural network consisting of a three-layer one-dimensional convolutional neural network (1D-CNN) and two-layer attention- based bidirectional long short-term memory (Bi-LSTM) network to learn representations from temporally sampled signals. The second stream extracts latent representations from the PPG time-frequency spectrogram using a five-layer CNN. The outputs from both streams are fed into a fusion layer for the outcome. Visualization of the attention weights learned demonstrates the effectiveness of the attention mechanism against noise. The experimental results show that the TSHNN outperforms all the competitive baseline approaches and with 98.09% accuracy, achieves state-of-the-art performance.

Keywords: PPG wearables, atrial fibrillation, feature fusion, attention mechanism, hyber network

Procedia PDF Downloads 121

Authors: Jing Hu, Dong Yang

Abstract:

With the increasingly difficult development and operating environment of exploration, there are many new challenges and difficulties in developing and exploiting oil and gas resources. These include the ability to dynamically monitor wells and provide data and assurance for the completion and production of high-cost and complex wells. A key technology in providing these assurances and maximizing oilfield profitability is real-time permanent reservoir monitoring. The emergence of optical fiber sensing systems has gradually begun to replace traditional electronic systems. Traditional temperature sensors can only achieve single-point temperature monitoring, but fiber optic sensing systems based on the Bragg grating principle have a high level of reliability, accuracy, stability, and resolution, enabling cost-effective monitoring, which can be done in real-time, anytime, and without well intervention. Continuous data acquisition is performed along the entire wellbore. The integrated package with the downhole pressure gauge, packer, and surface system can also realize real-time dynamic monitoring of the pressure in some sections of the downhole, avoiding oil well intervention and eliminating the production delay and operational risks of conventional surveys. Real-time information obtained through permanent optical fibers can also provide critical reservoir monitoring data for production and recovery optimization.

Keywords: PDHM, optical fiber, coiled tubing, photoelectric composite cable, digital-oilfield

Procedia PDF Downloads 79

Authors: Ahmad Umar, Sheikh Akbar, Ahmed A. Ibrahim, Mohsen A. Alhamami

Abstract:

The development of advanced materials for energy storage and gas sensing applications has gained significant attention in recent years. In this study, we synthesized and characterized PANI@MnO2@rGO ternary nanocomposites (NCs) to explore their potential in supercapacitors and gas sensing devices. The ternary NCs were synthesized through a multi-step process involving the hydrothermal synthesis of MnO2 nanoparticles, preparation of PANI@rGO composites and the assembly to the ternary PANI@MnO2@rGO ternary NCs. The structural, morphological, and compositional characteristics of the materials were thoroughly analyzed using techniques such as XRD, FESEM, TEM, FTIR, and Raman spectroscopy. In the realm of gas sensing, the ternary NCs exhibited excellent performance as NH3 gas sensors. The optimized operating temperature of 100 °C yielded a peak response of 15.56 towards 50 ppm NH3. The nanocomposites demonstrated fast response and recovery times of 6 s and 10 s, respectively, and displayed remarkable selectivity for NH3 gas over other tested gases. For supercapacitor applications, the electrochemical performance of the ternary NCs was evaluated using cyclic voltammetry and galvanostatic charge-discharge techniques. The composites exhibited pseudocapacitive behavior, with the capacitance reaching up to 185 F/g at 1 A/g and excellent capacitance retention of approximately 88.54% over 4000 charge-discharge cycles. The unique combination of rGO, PANI, and MnO2 nanoparticles in these ternary NCs offer synergistic advantages, showcasing their potential to address challenges in energy storage and gas sensing technologies.

Keywords: paniI@mnO2@rGO ternary NCs, synergistic effects, supercapacitors, gas sensing, energy storage

Procedia PDF Downloads 73

Authors: Jaiprakash Dargad

Abstract:

CdMnSe dilute semiconductor or semimagnetic semiconductors have become the focus of intense research due to their interesting combination of magnetic and semiconducting properties, and are employed in a variety of devices including solar cells, gas sensors etc. A series of thin films of this material, Cd1−xMnxSe (0 ≤ x ≤ 0.5), were therefore synthesized onto precleaned amorphous glass substrates using a solution growth technique. The sources of cadmium (Cd2+) and manganese (Mn2+) were aqueous solutions of cadmium sulphate and manganese sulphate, and selenium (Se2−) was extracted from a reflux of sodium selenosulphite. The different deposition parameters such as temperature, time of deposition, speed of mechanical churning, pH of the reaction mixture etc were optimized to yield good quality deposits. The as-grown samples were thin, relatively uniform, smooth and tightly adherent to the substrate support. The colour of the deposits changed from deep red-orange to yellowish-orange as the composition parameter, x, was varied from 0 to 0.5. The terminal layer thickness decreased with increasing value of, x. The optical energy gap decreased from 1.84 eV to 1.34 eV for the change of x from 0 to 0.5. The coefficient of optical absorption is of the order of 10-4 - 10-5 cm−1 and the type of transition (m = 0.5) is of the band-to-band direct type. The dc electrical conductivities were measured at room temperature and in the temperature range 300 K - 500 K. It was observed that the room temperature electrical conductivity increased with the composition parameter x up to 0.1, gradually decreasing thereafter. The thermo power measurements showed n-type conduction in these films.

Keywords: dilute semiconductor, reflux, CBD, thin film

Procedia PDF Downloads 231