Search results for: automatic sequential extraction device

Authors: Tarek Litim, Ouahiba Taamallah

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Burnishing is a mechanical surface treatment that combines several beneficial effects on the two steel grades studied. The application of burnishing to the ball or to the tip favors a better roughness compared to turning. In addition, it allows the consolidation of the surface layers through work hardening phenomena. The optimal effects are closely related to the treatment parameters and the active part of the device. With an improvement of 78% on the roughness, burnishing can be defined as a finishing operation in the machining range. With a 44% gain in consolidation rate, this treatment is an effective process for material consolidation. These effects are affected by several factors. The factors V, f, P, r, and i have the most significant effects on both roughness and hardness. Ball or tip burnishing leads to the consolidation of the surface layers of both grades 100C6 and 16NC6 steels by work hardening. For each steel grade and its mechanical treatment, the rational tensile curve has been drawn. Lüdwick's law is used to better plot the work hardening curve. For both grades, a material hardening law is established. For 100C6 steel, these results show a work hardening coefficient and a consolidation rate of 0.513 and 44, respectively, compared to the surface layers processed by turning. When 16NC6 steel is processed, the work hardening coefficient is about 0.29. Hardness tests characterize well the burnished depth. The layer affected by work hardening can reach up to 0.4 mm. Simulation of the tests is of great importance to provide the details at the local scale of the material. Conventional tensile curves provide a satisfactory indication of the toughness of 100C6 and 16NC6 materials. A simulation of the tensile curves revealed good agreement between the experimental and simulation results for both steels.

Keywords: 100C6 steel, 16NC6 steel, burnishing, work hardening, roughness, hardness

Procedia PDF Downloads 167

Authors: Jorge A. Cruz-Morales, Joel Vargas, Arlette A. Santiago, Mikhail A. Tlenkopatchev

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In industrial processes such as oil extraction and refining, products are handled or generated in the gas phase, which represents a challenge in terms of treatment and purification. During the past three decades, new scientific findings and technological advances in separation based on the use of membranes have led to simpler and more efficient gas separation processes, optimizing the use of energy and generating less pollution. This work reports the synthesis and ring-opening metathesis polymerization (ROMP) of new structural isomers based on norbornene dicarboximides bearing trifluoromethyl moieties, specifically N-2-trifluoromethylphenyl-exo,endo-norbornene-5,6-dicarboximide (2a) and N-3-trifluoromethylphenyl-exo,endo-norbornene-5,6-dicarboximide (2b), using tricyclohexylphosphine [1,3-bis(2,4,6-trimethylphenyl)-4,5-dihydroimidazol-2-ylidene][benzylidene] ruthenium dichloride (I), bis(tricyclohexylphosphine) benzylidene ruthenium (IV) dichloride (II), and bis(tricyclohexylphosphine) p-fluorophenylvinylidene ruthenium (II) dichloride (III). It was observed that the -CF3 moiety attached at the ortho position of the aromatic ring increases thermal and mechanical properties of the polymer, whereas meta substitution has the opposite effect. A comparative study of gas transportation in membranes, based on these fluorinated polynorbornenes, showed that -CF3 ortho substitution increases permeability of the polymer membrane as a consequence of the increase in both gas solubility and gas diffusion. In contrast, gas permeability coefficients of the meta-substituted polymer membrane are rather similar to those of that which is non-fluorinated; this can be attributed to a lower fractional free volume. The meta-substituted polymer membrane, besides showing the largest permselectivity coefficients of all the isomers studied here, was also found to have one of the largest permselectivity coefficients for separating H2/C3H6 into glassy polynorbornene dicarboximides.

Keywords: gas transport membranes, polynorbornene dicarboximide, ROMP, structural isomers

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Authors: Emmanuel Ophel Gilbert, Williams Speret

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The control for fluid flow behaviours of viscous fluids and heat transfer occurrences within heated mini-channel is considered. Heat transfer and flow characteristics of different viscous liquids, such as engine oil, automatic transmission fluid, one-half ethylene glycol, and deionized water were numerically analyzed. Some mathematical applications such as Fourier series and Laplace Z-Transforms were employed to ascertain the behaviour-wave like structure of these each viscous fluids. The steady, laminar flow and heat transfer equations are reckoned by the aid of numerical simulation technique. Further, this numerical simulation technique is endorsed by using the accessible practical values in comparison with the anticipated local thermal resistances. However, the roughness of this mini-channel that is one of the physical limitations was also predicted in this study. This affects the frictional factor. When an additive such as tetracycline was introduced in the fluid, the heat input was lowered, and this caused pro rata effect on the minor and major frictional losses, mostly at a very minute Reynolds number circa 60-80. At this ascertained lower value of Reynolds numbers, there exists decrease in the viscosity and minute frictional losses as a result of the temperature of these viscous liquids been increased. It is inferred that the three equations and models are identified which supported the numerical simulation via interpolation and integration of the variables extended to the walls of the mini-channel, yields the utmost reliance for engineering and technology calculations for turbulence impacting jets in the near imminent age. Out of reasoning with a true equation that could support this control for the fluid flow, Navier-stokes equations were found to tangential to this finding. Though, other physical factors with respect to these Navier-stokes equations are required to be checkmated to avoid uncertain turbulence of the fluid flow. This paradox is resolved within the framework of continuum mechanics using the classical slip condition and an iteration scheme via numerical simulation method that takes into account certain terms in the full Navier-Stokes equations. However, this resulted in dropping out in the approximation of certain assumptions. Concrete questions raised in the main body of the work are sightseen further in the appendices.

Keywords: frictional losses, heat transfer, laminar flow, mini-channel, number simulation, Reynolds number, turbulence, viscous fluids

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Authors: Xiang Zhang, Daohong Yang, Ziyuan Wu, Lei Li, Wanting Zhou

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Visual Simultaneous Localization and Mapping (VSLAM) uses cameras to collect information in unknown environments to realize simultaneous localization and environment map construction, which has a wide range of applications in autonomous driving, virtual reality and other related fields. At present, the related research achievements about VSLAM can maintain high accuracy in static environment. But in dynamic environment, due to the presence of moving objects in the scene, the movement of these objects will reduce the stability of VSLAM system, resulting in inaccurate localization and mapping, or even failure. In this paper, a robust VSLAM method was proposed to effectively deal with the problem in dynamic environment. We proposed a dynamic region removal scheme based on semantic segmentation neural networks and geometric constraints. Firstly, semantic extraction neural network is used to extract prior active motion region, prior static region and prior passive motion region in the environment. Then, the light weight frame tracking module initializes the transform pose between the previous frame and the current frame on the prior static region. A motion consistency detection module based on multi-view geometry and scene flow is used to divide the environment into static region and dynamic region. Thus, the dynamic object region was successfully eliminated. Finally, only the static region is used for tracking thread. Our research is based on the ORBSLAM3 system, which is one of the most effective VSLAM systems available. We evaluated our method on the TUM RGB-D benchmark and the results demonstrate that the proposed VSLAM method improves the accuracy of the original ORBSLAM3 by 70%˜98.5% under high dynamic environment.

Keywords: dynamic scene, dynamic visual SLAM, semantic segmentation, scene flow, VSLAM

Procedia PDF Downloads 116

Authors: Islam Abou El-Magd, Elham Ali, Moahmed Zakzouk, Nesreen Khairy, Naglaa Zanaty

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Maine environment and coastal zone are wealthy with natural resources that contribute to the local economy of Egypt. The Gulf of Suez and Red Sea area accommodates diverse human activities that contribute to the local economy, including oil exploration and production, touristic activities, export and import harbors, etc, however, it is always under the threat of pollution due to human interaction and activities. This research aimed at integrating in-situ measurements and remotely sensed data with hydrodynamic model to map and simulate the oil pollution. High-resolution satellite sensors including Sentinel 2 and Plantlab were functioned to trace the oil pollution. Spectral band ratio of band 4 (infrared) over band 3 (red) underpinned the mapping of the point source pollution from the oil industrial estates. This ratio is supporting the absorption windows detected in the hyperspectral profiles. ASD in-situ hyperspectral device was used to measure experimentally the oil pollution in the marine environment. The experiment used to measure water behavior in three cases a) clear water without oil, b) water covered with raw oil, and c) water after a while from throwing the raw oil. The spectral curve is clearly identified absorption windows for oil pollution, particularly at 600-700nm. MIKE 21 model was applied to simulate the dispersion of the oil contamination and create scenarios for crises management. The model requires precise data preparation of the bathymetry, tides, waves, atmospheric parameters, which partially obtained from online modeled data and other from historical in-situ stations. The simulation enabled to project the movement of the oil spill and could create a warning system for mitigation. Details of the research results will be described in the paper.

Keywords: oil pollution, remote sensing, modelling, Red Sea, Egypt

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Authors: Jigar Shaileshumar Halpati, Aravind Kumar Chandiran

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Vacancy ordered double perovskites (VOPs) have been significantly attracting researchers due to their chemical structure diversity and interesting optoelectronic properties. Some VOPs have been recently reported to be suitable photoelectrodes for photoelectrochemical water-splitting reactions due to their high stability and panchromatic absorption. In this work, we systematically synthesized mixed tetravalent VOPs based on Cs₂RuₘPt₁-ₘX₆ (X = Cl-, Br-) and reported their structural, optical, electrochemical and photoelectrochemical properties. The structural characterization confirms that the mixed tetravalent site intermediates formed their own phases. The parent materials, as well as their intermediates, were found to be stable in ambient conditions for over 1 year and also showed incredible stability in harsh pH media ranging from pH 1 to pH 11. Moreover, these materials showed panchromatic absorption with onset up to 1000 nm depending upon the mixture stoichiometry. The extraordinary stability and excellent absorption properties make them suitable materials for photoelectrochemical water-splitting applications. PEC studies of these series of materials showed a high water oxidation photocurrent of 0.56 mA cm-² for Cs₂Ru₀.₅Pt₀.₅Cl₆. Fundamental investigation from photoelectrochemical reactions revealed that the intrinsic ruthenium-based VOP showed enhanced hole transfer to the electrolyte, while the intrinsic platinum-based VOP showed higher photovoltage. The mix of these end members at the tetravalent site showed a synergic effect of reduced charge transfer resistance from the material to the electrolyte and increased photovoltage, which led to increased PEC performance of the intermediate materials.

Keywords: solar water splitting, photo electrochemistry, photo absorbers, material characterization, device characterization, green hydrogen

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Authors: Rajesh Agarwal

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Carrier mobility at the organic/insulator interface is essential to the performance of organic thin film transistors (OTFT). The present work describes estimation of field dependent mobility (FDM) parameters and the threshold voltage of an OTFT using a simple, easy to fabricate two terminal asymmetric capacitive test structure using admittance measurements. Conventionally, transfer characteristics are used to estimate the threshold voltage in an OTFT with field independent mobility (FIDM). Yet, this technique breaks down to give accurate results for devices with high contact resistance and having field dependent mobility. In this work, a new technique is presented for characterization of long channel organic capacitor (LCOC). The proposed technique helps in the accurate estimation of mobility enhancement factor (γ), the threshold voltage (V_th) and band mobility (µ₀) using capacitance-voltage (C-V) measurement in OTFT. This technique also helps to get rid of making short channel OTFT or metal-insulator-metal (MIM) structures for making C-V measurements. To understand the behavior of devices and ease of analysis, transmission line compact model is developed. The 2-D numerical simulation was carried out to illustrate the correctness of the model. Results show that proposed technique estimates device parameters accurately even in the presence of contact resistance and field dependent mobility. Pentacene/Poly (4-vinyl phenol) based top contact bottom-gate OTFT’s are fabricated to illustrate the operation and advantages of the proposed technique. Small signal of frequency varying from 1 kHz to 5 kHz and gate potential ranging from +40 V to -40 V have been applied to the devices for measurement.

Keywords: capacitance, mobility, organic, thin film transistor

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Authors: Usma Iftikhar, Khorshed Alam

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Background: Digital health platforms and digital health interventions are gaining increasing importance with the shift to online health-seeking behaviour, especially post-Covid. Subsequently, the importance of digital health literacy is increasingly being recognized. With the surge in culturally and linguistically diverse populations in First World countries, especially females, the predictors of digital health access in this population remain elusive. Keeping in view the inadequate digital infrastructure in rural and remote Australia, with lack of specialist services, the determinants of digital access gain even more importance. Objectives: The objective of this research are to measure the digital health literacy levels in this population, including the predictors of digital health literacy like sociodemographics and the correlation between the predictors and digital health literacy levels. Methods: A population-based quantitative survey was carried out in Regional Queensland from Jan 2022- Dec 2023 on culturally and linguistically diverse adult females. Sociodemographics like age, literacy levels, socioeconomic status, access to digital devices were recorded after informed consent. Digital health literacy levels were measured by specially designed questionnaires. The relationship between sociodemographics and digital health literacy levels was estimated by Pearson correlation. Results: Mean DHL was 2.66 + 0.35. There was a negative significant relationship (p<0.005) between demographics like age and access to a digital device with digital health literacy levels. Also observed was a positive significant relationship between literacy levels and proficiency in English. Conclusion: Age, literacy levels and English proficiency are some of the highest predictors of digital health access. This is important because remote areas rely on digital health access due to less developed health infrastructure, including specialist services. Guide for Policy makers to focus on the populations most in need.

Keywords: digital health literacy, eHealth literacy, culturally and linguistically diverse, ethnic minorities, regional areas, rural and remote areas

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Authors: Anima Ashraf

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Over the past few decades biodiversity has become the issue of global concern for its rapid reduction worldwide. Bangladesh is no exception. The country is exceptionally endowed with a vast variety of flora and fauna, but due to tremendous population pressure, rural poverty and unemployment it has been decreased alarmingly. Since, both biodiversity and sustainable development are the part of human life in modern era and both work together to make our life safer and comfortable therefore balance should be kept in development and biodiversity conservation and priority should be given to alternative and sustainable development paths. This paper is based on study of two projects undertaken by Arannayk Foundation jointly with its local NGO partners. The aim was to understand previous, current and future scenarios for the hilly biodiversity of Khagrachari in the Chittagong Hill Tracts (CHT) of Bangladesh. It is also observed how alternative income generating activities (AIGA) improve livelihood of the tribal inhabitants of the area, decrease their dependency on forest resources and also aid conservation activities. Intensive field visits were made and interviews were conducted with key informants to see the progress and achievements of local NGOs working with the tribal community for the past seven years to restore the denuded hills of Khagrachari. The paper also covers the impacts and interventions of the projects and the methods used to aid conservation activities. Raising awareness among the villagers has reduced extraction of forests resources by 47% and granting funds and access to microcredit to adopt AIGAs have increased their average annual income by 25%. Finally, the paper concludes that effective community-based conservation practices are fundamental to ensure biodiversity conservation in the Chittagong Hill Tracts. In order to conserve biodiversity and restore the forests of CHT, livelihood development of the villagers has to be considered as the main component of the projects undertaken by all NGOs and the Government.

Keywords: biodiversity, conservation, forests, livelihood

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: M. Torres, A. Moulay, M. Zhuldybina, M. Rozel, N. D. Trinh, C. Bois

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Printed electronics are a fast-growing market as their applications cover a large range of industrial needs, their production cost is low, and the additive printing techniques consume less materials than subtractive manufacturing methods used in traditional electronics. With the growing demand for printed electronics, there are concerns about their harmful and irreversible contribution to the environment. Indeed, it is estimated that 80% of the environmental load of a product is determined by the choices made at the conception stage. Therefore, examination through a life cycle approach at the developing stage of a novel product is the best way to identify potential environmental issues and make proactive decisions. Life cycle analysis (LCA) is a comprehensive scientific method to assess the environmental impacts of a product in its different stages of life: extraction of raw materials, manufacture and distribution, use, and end-of-life. Impacts and major hotspots are identified and evaluated through a broad range of environmental impact categories of the ReCiPe (H) middle point method. At the conception stage, the LCA is a tool that provides an environmental point of view on the choice of materials and processes and weights-in on the balance between performance materials and eco-friendly materials. Using the life cycle approach, the current work aims to provide a cradle-to-grave life cycle assessment of a roll-to-roll hybrid printed smart label designed for the food cold chain. Furthermore, this presentation will present the environmental impact of metallic conductive inks, a comparison with promising conductive polymers, evaluation of energy vs. performance of industrial printing processes, a full assessment of the impact from the smart label applied on a cellulosic-based substrate during the recycling process and the possible recovery of precious metals and rare earth elements.

Keywords: Eco-design, label, life cycle assessment, printed electronics

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Felice De Luca, Vincenzo Galdi, Piera Stella, Vito Calderaro, Adriano Campagna, Antonio Piccolo

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Sustainable mobility has become one of the major issues of recent years. The challenge in reducing polluting emissions as much as possible has led to the production and diffusion of vehicles with internal combustion engines that are less polluting and to the adoption of green energy vectors, such as vehicles powered by natural gas or LPG and, more recently, with hybrid and electric ones. While on the one hand, the spread of electric vehicles for private use is becoming a reality, albeit rather slowly, not the same is happening for vehicles used for public transport, especially those that operate in the congested areas of the cities. Even if the first electric buses are increasingly being offered on the market, it remains central to the problem of autonomy for battery fed vehicles with high daily routes and little time available for recharging. In fact, at present, solid-state batteries are still too large in size, heavy, and unable to guarantee the required autonomy. Therefore, in order to maximize the energy management on the vehicle, the optimization of driving profiles offer a faster and cheaper contribution to improve vehicle autonomy. In this paper, following the authors’ precedent works on electric vehicles in public transport and energy management strategies in the electric mobility area, an eco-driving strategy for electric bus is presented and validated. Particularly, the characteristics of the prototype bus are described, and a general-purpose eco-drive methodology is briefly presented. The model is firstly simulated in MATLAB™ and then implemented on a mobile device installed on-board of a prototype bus developed by the authors in a previous research project. The solution implemented furnishes the bus-driver suggestions on the guide style to adopt. The result of the test in a real case will be shown to highlight the effectiveness of the solution proposed in terms of energy saving.

Keywords: eco-drive, electric bus, energy management, prototype

Procedia PDF Downloads 139

Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

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Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

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Authors: H. M. D. Prabhashana Herath, M. D. Anuradha Wickramasinghe, A. M. C. Kalpani Polgolla, R. A. C. Prasad Ranasinghe, M. Anusha Wijewardane

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The demand for thermal comfort in buildings in hot and humid climates increases progressively. In general, buildings in hot and humid climates spend more than 60% of the total energy cost for the functionality of the air conditioning (AC) system. Hence, it is required to install energy efficient AC systems or integrate energy recovery systems for both new and/or existing AC systems whenever possible, to reduce the energy consumption by the AC system. Integrate a Rotary Thermal Wheel as the energy recovery device of an existing AC system has shown very promising with attractive payback periods of less than 5 years. A rotary thermal wheel can be located in the Air Handling Unit (AHU) of a central AC system to recover the energy available in the return air stream. During this study, a sensitivity analysis was performed using a CFD (Computational Fluid Dynamics) software to determine the optimum design parameters (i.e., rotary speed and parameters of the matrix profile) of a rotary thermal wheel for hot and humid climates. The simulations were performed for a sinusoidal matrix geometry. Variation of sinusoidal matrix parameters, i.e., span length and height, were also analyzed to understand the heat exchanging performance and the induced pressure drop due to the air flow. The results show that the heat exchanging performance increases when increasing the wheel rpm. However, the performance increment rate decreases when increasing the rpm. As a result, it is more advisable to operate the wheel at 10-20 rpm. For the geometry, it was found that the sinusoidal geometries with lesser spans and higher heights have higher heat exchanging capabilities. Considering the sinusoidal profiles analyzed during the study, the geometry with 4mm height and 3mm width shows better performance than the other combinations.

Keywords: air conditioning, computational fluid dynamics, CFD, energy recovery, heat exchangers

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Authors: Sohal Latif

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Solar power is in the form of photovoltaic, also known as PV, which is a form of renewable energy that applies solar panels in producing electricity from the sun. It has a vital role in fulfilling the present need for clean and renewable energy to get rid of conventional and non-renewable energy sources that emit high levels of greenhouse gases. Solar energy is embraced because of its availability, easy accessibility, and effectiveness in the provision of power, chiefly in country areas. In solar charging, device charge entails a change of light power into electricity using photovoltaic or PV panels, which supply direct current electric power or DC. Here, the solar charge controller has a very crucial role to play regarding the voltages and the currents coming from the solar panels to take up the changing needs of a battery without overcharging the same. Certain devices, such as inverters, are required to transform the DC power produced by the solar panels into an AC to serve the normal electrical appliances and the current power network. This project was initiated for a project of a solar charge controller and power converter with the MULTISIM. The formation of this project begins with a literature survey to obtain basic knowledge about power converters, charge controllers, and photovoltaic systems. Fundamentals of the operation of solar panels include the process by which light is converted into electricity and a comparison of PWM and MPPT chargers with controllers. Knowledge of rectifiers is built to help achieve AC-to-DC and DC-AC change. Choosing a resistor, capacitance, MOSFET, and OP-AMP is done by the need of the system. The circuit diagrams of converters and charge controllers are designed using the Multisim program. Pulse width modulation, Bubba oscillator circuit, and inverter circuits are modeled and simulated. In the subsequent steps, the analysis of the simulation outcomes indicates the efficiency of the intended converter systems. The various outputs from the different configurations, with the transformer incorporated as well as without it, are then monitored for effective power conversion as well as power regulation.

Keywords: solar charge controller, MULTISIM, converter, inverter

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Authors: Riaz A. Agha, Alexander J. Fowler, Seon-Young Lee, Buket Gundogan, Katharine Whitehurst, Harkiran K. Sagoo, Kyung Jin Lee Jeong, Douglas G. Altman, Dennis P. Orgill

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Introduction: Case Series are an important and common study type. Currently, no guideline exists for reporting case series and there is evidence of key data being missed from such reports. We propose to develop a reporting guideline for case series using a methodologically robust technique. The first step in this process is a systematic review of literature relevant to the reporting deficiencies of case series. Methods: A systematic review of methodological and reporting quality in surgical case series was performed. The electronic search strategy was developed by an information specialist and included MEDLINE, EMBASE, Cochrane Methods Register, Science Citation index and Conference Proceedings Citation index, from the start of indexing until 5th November 2014. Independent screening, eligibility assessments and data extraction was performed. Included articles were analyzed for five areas of deficiency: failure to use standardized definitions missing or selective data transparency or incomplete reporting whether alternate study designs were considered. Results: The database searching identified 2,205 records. Through the process of screening and eligibility assessments, 92 articles met inclusion criteria. Frequency of methodological and reporting issues identified was a failure to use standardized definitions (57%), missing or selective data (66%), transparency, or incomplete reporting (70%), whether alternate study designs were considered (11%) and other issues (52%). Conclusion: The methodological and reporting quality of surgical case series needs improvement. Our data shows that clear evidence-based guidelines for the conduct and reporting of a case series may be useful to those planning or conducting them.

Keywords: case series, reporting quality, surgery, systematic review

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: Amr A. El Hanafy, Muhammad I. Qureshi, Jamal Sabir, Mohamed Mutawakil, Mohamed M. Ahmed, Hassan El Ashmaoui, Hassan Ramadan, Mohamed Abou-Alsoud, Mahmoud Abdel Sadek

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β-Lactoglobulin (β-LG) is the dominant non-casein whey protein found in bovine milk and of most ruminants. The amino acid sequence of β-LG along with its 3-dimensional structure illustrates linkage with the lipocalin superfamily. Preliminary studies in goats indicated that milk yield can be influenced by polymorphism in genes coding for whey proteins. The aim of this study is to identify and evaluate the incidence of functional polymorphisms in the exonic and intronic portions of β-LG gene in native Saudi goat breeds (Ardi, Habsi, and Harri). Blood samples were collected from 300 animals (100 for each breed) and genomic DNA was extracted using QIAamp DNA extraction Kit. A fragment of the β-LG gene from exon 7 to 3’ flanking region was amplified with pairs of specific primers. Subsequent digestion with Sac II restriction endonuclease revealed two alleles (A and B) and three different banding patterns or genotypes i.e. AA, AB and BB. The statistical analysis showed that β-LG AA genotype had higher milk yield than β-LG AB and β-LG BB genotypes. Nucleotide sequencing of the selected β-LG fragments was done and submitted to GenBank NCBI (Accession No. KJ544248, KJ588275, KJ588276, KJ783455, KJ783456 and KJ874959). Two already established SNPs in exon 7 (+4601 and +4603) and one fresh SNP in the 3’ UTR region were detected in the β-LG fragments with designated AA genotype. The polymorphisms in exon 7 did not produce any amino acid change. Phylogenetic analysis on the basis of nucleotide sequences of native Saudi goats indicated evolutional similarity with the GenBank reference sequences of goat, Bubalus bubalis and Bos taurus.

Keywords: β-Lactoglobulin, Saudi goats, PCR-RFLP, functional polymorphism, nucleotide sequencing, phylogenetic analysis

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Authors: Harish Aryal

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The safe and reliable operation of nuclear reactors has always been one of the topmost priorities in the nuclear industry. Transient testing allows us to understand the time-dependent behavior of the neutron population in response to either a planned change in the reactor conditions or unplanned circumstances. These unforeseen conditions might occur due to sudden reactivity insertions, feedback, power excursions, instabilities, and accidents. To study such behavior, we need transient testing, which is like car crash testing, to estimate the durability and strength of a car design. In nuclear designs, such transient testing can simulate a wide range of accidents due to sudden reactivity insertions and helps to study the feasibility and integrity of the fuel to be used in certain reactor types. This testing involves a high neutron flux environment and real-time imaging technology with advanced instrumentation with appropriate accuracy and resolution to study the fuel slumping behavior. With the aid of transient testing and adequate imaging tools, it is possible to test the safety basis for reactor and fuel designs that serves as a gateway in licensing advanced reactors in the future. To that end, it is crucial to fully understand advanced imaging techniques both analytically and via simulations. This paper presents an innovative method of supporting real-time imaging of fuel pins and other structures during transient testing. The major fuel-motion detection device that is studied in this dissertation is the Hodoscope which requires collimators. This paper provides 1) an MCNP model and simulation of a Transient Reactor Test (TREAT) core with a central fuel element replaced by a slotted fuel element that provides an open path between test samples and a hodoscope detector and 2) a choice of good filter to improve image resolution.

Keywords: hodoscope, transient testing, collimators, MCNP, TREAT, hodogram, filters

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Authors: Basant Singh Sikarwar, Mukesh Roy, Ayush Goyal, Priya Ranjan

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This work combines two bodies of knowledge which includes biomedical basis of blood stain formation and fluid communities’ wisdom that such formation of blood stain depends heavily on physical properties. Moreover biomedical research tells that different patterns in stains of blood are robust indicator of blood donor’s health or lack thereof. Based on these valuable insights an innovative screening tool is proposed which can act as an aide in the diagnosis of diseases such Anemia, Hyperlipidaemia, Tuberculosis, Blood cancer, Leukemia, Malaria etc., with enhanced confidence in the proposed analysis. To realize this powerful technique, simple, robust and low-cost micro-fluidic devices, a micro-capillary viscometer and a pendant drop tensiometer are designed and proposed to be fabricated to measure the viscosity, surface tension and wettability of various blood samples. Once prognosis and diagnosis data has been generated, automated linear and nonlinear classifiers have been applied into the automated reasoning and presentation of results. A support vector machine (SVM) classifies data on a linear fashion. Discriminant analysis and nonlinear embedding’s are coupled with nonlinear manifold detection in data and detected decisions are made accordingly. In this way, physical properties can be used, using linear and non-linear classification techniques, for screening of various diseases in humans and cattle. Experiments are carried out to validate the physical properties measurement devices. This framework can be further developed towards a real life portable disease screening cum diagnostics tool. Small-scale production of screening cum diagnostic devices is proposed to carry out independent test.

Keywords: blood, physical properties, diagnostic, nonlinear, classifier, device, surface tension, viscosity, wettability

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Authors: E. N. Masri, E. Takács

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Amplitude Versus Offset (AVO) and simultaneous model-based impedance inversion techniques have not been utilized for geothermal exploration commonly; however, some recent publications called the attention that they can be very useful in the geothermal investigations. In this study, we present rock physical attributes obtained from 3D pre-stack seismic data and well logs collected in a study area of the NW part of Pannonian Basin where the geothermal reservoir is located in the fractured zones of Triassic basement and it was hit by three productive-injection well pairs. The holes were planned very successfully based on the conventional 3D migrated stack volume prior to this study. Subsequently, the available geophysical-geological datasets provided a great opportunity to test modern inversion procedures in the same area. In this presentation, we provide a summary of the theory and application of the most promising seismic inversion techniques from the viewpoint of geothermal exploration. We demonstrate P- and S-wave impedance, as well as the velocity (Vp and Vs), the density, and the Vp/Vs ratio attribute volumes calculated from the seismic and well-logging data sets. After a detailed discussion, we conclude that P-wave impedance and Vp/Vp ratio are the most helpful parameters for lithology discrimination in the study area. They detect the hot water saturated fracture zone very well thus they can be very useful in mapping the investigated reservoir. Integrated interpretation of all the obtained rock-physical parameters is essential. We are extending the above discussed pre-stack seismic tools by studying the possibilities of Elastic Impedance Inversion (EII) for geothermal exploration. That procedure provides two other useful rock-physical properties, the compressibility and the rigidity (Lamé parameters). Results of those newly created elastic parameters will also be demonstrated in the presentation. Geothermal extraction is of great interest nowadays; and we can adopt several methods have been successfully applied in the hydrocarbon exploration for decades to discover new reservoirs and reduce drilling risk and cost.

Keywords: fractured zone, seismic, well-logging, inversion

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Authors: Yusif Habib

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The Volta River Basin (VRB) is a transboundary resource shared by Six (6) the West African States. It’s utilization spans across irrigation, hydropower generation, domestic/household water use, transportation, industrial processing, among others. Simultaneously, mineral resources such as gold are mined within the VRB catchment. Typically, the extraction/mining operation is earth-surface excavation; known as Artisanal and Small-scale mining. We developed a conceptual framework in the context of Water-Energy-Food (WEF) Nexus to delineate the trade-offs and synergies between the mineral extractive operation’s impact on Agricultural systems, specifically, cereal crops (e.g. Maize, Millet, and Rice) and the environment (water and soil quality, deforestation, etc.) on the VRB. Thus, the study examined the trade-offs and synergies through the WEF nexus lens to explore the extent of an eventual overarching mining preference for gold exploration with high economic returns as opposed to the presumably low yearly harvest and household income from food crops production to inform intervention prioritization. Field survey (household, expert, and stakeholder consultation), bibliometric analysis/literature review, scenario, and simulation models, including land-use land cover (LULC) analyses, were conducted. The selected study area(s) in Ghana was the location where the mineral extractive operation’s presence and impact are widespread co-exist with the Agricultural systems. Overall, the study proposes mechanisms of the virtuous cycle through FEW Nexus instead of the presumably existing vicious cycle to inform decision making and policy implementation.

Keywords: agriculture, environmental sustainability, gold Mining, synergies, trade-off, water-energy-food nexus

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Authors: Varinder Kaur, Amanjit Kaur, Simran Kaur, Samriti Vaid

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Coating of fabrics with anti-microbial dyes is an adaptable technique of protection from various diseases. Natural dyes, which are known to possess antibacterial properties, can be used for antibacterial finishing of fibers like cotton, wool, bamboo and so many. Dyeing of fabrics with natural dyes normally requires the use of mordants so that dyes can stay on the fabric as well as into interstices of the fabric during multiple washings. In this study, the mordants used are alum and chitosan for ensuring a reasonable color fastness to light and washing. Chitosan is a natural polysaccharide having significant biological and chemical properties such as biodegradability, biocompatibility, bioactivity, microbial activity and polycationicity. The metal ion of alum mordant can act as electron acceptor for electron donor to form coordination bond with the dye molecule, making them insoluble in water. The dyeing of bamboo fabric using a natural dye extracted from turmeric has been studied using conventional dyeing method. Natural dye was extracted using water as solvent by Soxhlet extraction method. The extracted color was characterized by spectroscopic studies like UV/visible and further tested for antimicrobial activity. The effect of mordants on the dyeing outcome in terms of colour depth as well as fastness properties of the dyeing was investigated. It has been found that employing the conventional dyeing technique at 100 oC, the mordanted samples were deeper in depth than their unmordanted counterparts. The results of fastness properties of the dyed fabrics were fair to good. Turmeric extract was found to enhance microbial resistance of bamboo as well as was itself as a good cause of coloration. These textiles dyed with the turmeric as natural dye can be very useful in developing clothing for infants, elderly and infirm people to protect them against common infections. The outcome of this study will provide a new feature to the interface of dyeing and pharmaceutical industry.

Keywords: antimicrobial activity, bamboo fabric, natural dye, turmeric

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Authors: Manal Alhazmi, Artem Mishchenko

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A molecular fluids' behavior and interaction with other materials at the nanoscale is a complex process. Nanoscale fluids behave so differently than macroscale fluids and interact with other materials in unique ways. It is, therefore, feasible to understand the molecular behavior of H₂O in such two-dimensional nanoscale systems by studying (CaSO4-2H2O), commonly known as gypsum. In the present study, spectroscopic measurements on a 2D structure of exfoliated gypsum crystals are carried out by Raman and IR spectroscopy. An array of gypsum flakes with thicknesses ranging from 8nm to 100nm were observed and analyzed for their Raman and IR spectrum. Water molecules stretching modes spectra lines were also measured and observed in nanoscale gypsum flakes and compared with those of bulk crystals. CaSO4-2H2O crystals have Raman and infrared bands at 3341 cm-1 resulting from the weak hydrogen bonds between the water molecules. This internal vibration of water molecules, together with external vibrations with other atoms, are responsible for these bands. There is a shift of about 70 cm-1 In the peak position of thin flakes with respect to the bulk crystal, which is a result of the different atomic arrangement from bulk to thin flake on the nano scale. An additional peak was observed in Raman spectra around 2910-3137 cm⁻¹ in thin flakes but is missing in bulk crystal. This additional peak is attributed to a combined mode of water internal (stretching mode at 3394cm⁻¹) and external vibrations. In addition to Raman and infra- red analysis of gypsum 2D structure, electrical measurements were conducted to reveal the water molecules transport behavior in such systems. Electrical capacitance of the fabricated device is measured and found to be (0.0686 *10-12) F, and the calculated dielectric constant (ε) is (12.26).

Keywords: gypsum, infra-red spectroscopy, raman spectroscopy, H₂O behavior

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Authors: Alok Madan, Arshad K. Hashmi

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The paper presents a plastic analysis procedure based on the energy balance concept for performance based seismic retrofit of multi-story multi-bay masonry infilled reinforced concrete (R/C) frames with a ‘soft’ ground story using passive energy dissipation (PED) devices with the objective of achieving a target performance level of the retrofitted R/C frame for a given seismic hazard level at the building site. The proposed energy based plastic analysis procedure was employed for developing performance based design (PBD) formulations for PED devices for a simulated application in seismic retrofit of existing frame structures designed in compliance with the prevalent standard codes of practice. The PBD formulations developed for PED devices were implemented for simulated seismic retrofit of a representative code-compliant masonry infilled R/C frame with a ‘soft’ ground story using friction dampers as the PED device. Non-linear dynamic analyses of the retrofitted masonry infilled R/C frames is performed to investigate the efficacy and accuracy of the proposed energy based plastic analysis procedure in achieving the target performance level under design level earthquakes. Results of non-linear dynamic analyses demonstrate that the maximum inter-story drifts in the masonry infilled R/C frames with a ‘soft’ ground story that is retrofitted with the friction dampers designed using the proposed PBD formulations are controlled within the target drifts under near-field as well far-field earthquakes.

Keywords: energy methods, masonry infilled frame, near-field earthquakes, seismic protection, supplemental damping devices

Procedia PDF Downloads 297

Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

Procedia PDF Downloads 162

Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

Procedia PDF Downloads 95

Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

Procedia PDF Downloads 154