Search results for: behavior against washing machine parameters

Authors: Joseph C. Chen

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Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.

Keywords: DMAIC, machine vision system, process capability, Taguchi Parameter Design

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

Procedia PDF Downloads 115

Authors: Darwin Junnior Sabino Diego, Brando Burgos Guerrero, Diego Arroyo Villanueva

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Additive manufacturing, commonly known as 3D printing, has revolutionized modern manufacturing by enabling the agile creation of complex objects. However, challenges persist in the consistency and quality of printed parts, particularly in their mechanical properties. This study focuses on addressing these challenges through the optimization of printing parameters in FDM technology, using Machine Learning techniques. Our aim is to improve the mechanical properties of printed objects by optimizing parameters such as speed, temperature, and orientation. We implement a methodology that combines experimental data collection with Machine Learning algorithms to identify relationships between printing parameters and mechanical properties. The results demonstrate the potential of this methodology to enhance the quality and consistency of 3D printed products, with significant applications across various industrial fields. This research not only advances understanding of additive manufacturing but also opens new avenues for practical implementation in industrial settings.

Keywords: 3D printing, additive manufacturing, machine learning, mechanical properties

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Authors: Saeedeh Bakhtiari, Johannes Depessemier, Stijn Hertelé, Wim De Waele

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High cycle fatigue comprising up to 10⁷ load cycles has been the subject of many studies, and the behavior of many materials was recorded adequately in this regime. However, many applications involve larger numbers of load cycles during the lifetime of machine components. In this ultra-high cycle regime, other failure mechanisms play, and the concept of a fatigue endurance limit (assumed for materials such as steel) is often an oversimplification of reality. When machine component design demands a high geometrical complexity, cast iron grades become interesting candidate materials. Grey cast iron is known for its low cost, high compressive strength, and good damping properties. However, the ultra-high cycle fatigue behavior of cast iron is poorly documented. The current work focuses on the ultra-high cycle fatigue behavior of EN-GJL-250 (GG25) grey cast iron by developing an ultrasonic (20 kHz) fatigue testing system. Moreover, the testing machine is instrumented to measure the temperature and the displacement of  the specimen, and to control the temperature. The high resonance frequency allowed to assess the  behavior of the cast iron of interest within a matter of days for ultra-high numbers of cycles, and repeat the tests to quantify the natural scatter in fatigue resistance.

Keywords: GG25, cast iron, ultra-high cycle fatigue, ultrasonic test

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Walter Evaldo Kuchenbecker, Julio Carlos Teixeira

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Since efficiency became a challenge to reduce energy consumption of all electrical machines applications, the permanent magnet machine raises up as a better option, because its performance, robustness and simple control. Even though, the electrical machine was developed through analyses of magnetism effect, permanent magnet machines still not well dominated. As permanent magnet machines are becoming popular in most applications, the pressure to standardize this type of electrical machine increases. However, due limited domain, it is still nowadays without any standard to manufacture, test and application. In order to determine an inductance of the machine, a new method is proposed.

Keywords: permanent magnet generators (pmg), synchronous machine parameters, test procedures, inductances

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Authors: Ahmed A. D. Sarhan, S. R. Besharaty, Javad Akbaria, M. Hamdi

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The capability of CNC gantry milling machines in manufacturing long components has caused the expanded use of such machines. On the other hand, the machines’ gantry rigidity can reduce under severe loads or vibration during operation. Indeed, the quality of machining is dependent on the machine’s dynamic behavior throughout the operating process. For this reason, this type of machines has always been used prudently and are non efficient. Therefore, they can usually be employed for rough machining and may not produce adequate surface finishing. In this paper, a CNC gantry milling machine with the potential to produce good surface finish has been designed and analyzed. The lowest natural frequency of this machine is 202 Hz at all motion amplitudes with a full range of suitable frequency responses. Meanwhile, the maximum deformation under dead loads for the gantry machine is 0.565µm, indicating that this machine tool is capable of producing higher product quality.

Keywords: frequency response, finite element, gantry machine, gantry design, static and dynamic analysis

Procedia PDF Downloads 357

Authors: Enyia James Diwa, Isaiah Thank-God Ebi, Dodeye Ina Igbong

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The privatization of utilities has brought about very strong competition in industries such as petrochemical and gas distribution among others, considering the continuous increase in cost of fuel. This has brought about the intense reason for gas turbine owners and operators to reduce and control performance degradation of the engine in other to minimize cost. The most common and very crucial problem of the gas turbine is the fouling of compressor, which is mostly caused by a reduction in flow capacity, compressor efficiency, and pressure ratio, this, in turn, lead to the engine compressor re-matching and output power and thermal efficiency reduction. The content of this paper encompasses a detailed presentation of the major causes, effects and control mechanism of fouling. The major emphasis is on compressor water washing to enable power augmentation. A modelled gas turbine similar to that of GE LM6000 is modelled for the current study, based on TURBOMATCH which is a Cranfield University software specifically made for gas turbine performance simulation and fouling detection. The compounded and intricate challenges of compressor online water washing of large output gas turbine are carried out. The treatment is applied to axial compressor used in the petrochemical and hydrocarbon industry.

Keywords: gas turbine, fouling, degradation, compressor washing

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Authors: Precious Ehiomogue, Ifechukwude Israel Ahuchaogu, Isiguzo Edwin Ahaneku

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Vermicompost is the product of the decomposition process using various species of worms, to create a mixture of decomposing vegetable or food waste, bedding materials, and vemicast. This process is called vermicomposting, while the rearing of worms for this purpose is called vermiculture. Several works have verified the adsorption of toxic metals using vermicompost but the application is still scarce for the retention of organic compounds. This research brings to knowledge the effectiveness of earthworm waste (vermicompost) for the remediation of crude oil contaminated soils. The remediation methods adopted in this study were two soil washing methods namely, batch and column process which represent laboratory and in-situ remediation. Characterization of the vermicompost and crude oil contaminated soil were performed before and after the soil washing using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF), X-ray diffraction (XRD) and Atomic adsorption spectrometry (AAS). The optimization of washing parameters, using response surface methodology (RSM) based on Box-Behnken Design was performed on the response from the laboratory experimental results. This study also investigated the application of machine learning models [Artificial neural network (ANN), Adaptive neuro fuzzy inference system (ANFIS). ANN and ANFIS were evaluated using the coefficient of determination (R²) and mean square error (MSE)]. Removal efficiency obtained from the Box-Behnken design experiment ranged from 29% to 98.9% for batch process remediation. Optimization of the experimental factors carried out using numerical optimization techniques by applying desirability function method of the response surface methodology (RSM) produce the highest removal efficiency of 98.9% at absorbent dosage of 34.53 grams, adsorbate concentration of 69.11 (g/ml), contact time of 25.96 (min), and pH value of 7.71, respectively. Removal efficiency obtained from the multilevel general factorial design experiment ranged from 56% to 92% for column process remediation. The coefficient of determination (R²) for ANN was (0.9974) and (0.9852) for batch and column process, respectively, showing the agreement between experimental and predicted results. For batch and column precess, respectively, the coefficient of determination (R²) for RSM was (0.9712) and (0.9614), which also demonstrates agreement between experimental and projected findings. For the batch and column processes, the ANFIS coefficient of determination was (0.7115) and (0.9978), respectively. It can be concluded that machine learning models can predict the removal of crude oil from polluted soil using vermicompost. Therefore, it is recommended to use machines learning models to predict the removal of crude oil from contaminated soil using vermicompost.

Keywords: ANFIS, ANN, crude-oil, contaminated soil, remediation and vermicompost

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Authors: C. Schneider, M. M. Seizmeir, T. Suchanek, M. Hutter-Mironovova, M. Bdiwi, M. Putz

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In this paper, a suitable measurement setup is presented to conduct force and pressure measurements for transient contact cases at the example of lathe machine tending. Empirical measurements were executed on a selected collaborative robot’s behavior regarding allowable operating speeds under consideration of sensor- and workpiece-specific factors. Comparisons between the theoretic calculations proposed in ISO/TS 15066 and the practical measurement results reveal a basis for future research. With the created database, preliminary risk assessment and economic assessment procedures of collaborative machine tending cells can be facilitated.

Keywords: biomechanical thresholds, collaborative robots, force and pressure measurements, machine tending, transient contact

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Authors: Marcel Lehr, Andreas Binder

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This paper compares three different permanent magnet synchronous machines (Doubly-Salient-Permanent-Magnet-Machine (DSPM), Flux-Reversal-Permanent-Magnet-Machine (FRPM), Flux-Switching-Permanent-Magnet-Machine (FSPM)) with the permanent magnets in the stator of the machine for use as an industrial drive for 400 V Y, 45 kW and 1000 ... 3000 min-1. The machines are compared based on the magnetic co-energy and Finite-Element-Method-Simulations regarding the torque density. The results show that the FSPM provides the highest torque density of the three machines. Therefore, an FSPM prototype was built, tested on a test bench and finally compared with an already built conventional permanent magnet synchronous machine (PMSM) of the same size (stator outer diameter dso = 314 mm, axial length lFe = 180 mm) and rating with surface-mounted rotor magnets. These measurements show that the conventional PMSM and the FSPM machine are roughly equivalent in their electrical behavior.

Keywords: doubly-salient-permanent-magnet-machine, flux-reversal-permanent-magnet-machine, flux-switching-permanent-magnet-machine, industrial drive

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Authors: C. Brecher, M. Fey, F. Du Bois-Reymond, S. Neus

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Additive manufacturing has emerged into a fast-growing section within the manufacturing technologies. Established machine tool manufacturers, such as DMG MORI, recently presented machine tools combining milling and laser welding. By this, machine tools can realize a higher degree of flexibility and a shorter production time. Still there are challenges that have to be accounted for in terms of maintaining the necessary machining accuracy - especially due to thermal effects arising through the use of high power laser processing units. To study the thermal behavior of laser-integrated machine tools, it is essential to analyze and simulate the thermal behavior of machine components, individual and assembled. This information will help to design a geometrically stable machine tool under the influence of high power laser processes. This paper presents an approach to decrease the loss of machining precision due to thermal impacts. Real effects of laser machining processes are considered and thus enable an optimized design of the machine tool, respective its components, in the early design phase. Core element of this approach is a matched FEM model considering all relevant variables arising, e.g. laser power, angle of laser beam, reflective coefficients and heat transfer coefficient. Hence, a systematic approach to obtain this matched FEM model is essential. Indicating the thermal behavior of structural components as well as predicting the laser beam path, to determine the relevant beam intensity on the structural components, there are the two constituent aspects of the method. To match the model both aspects of the method have to be combined and verified empirically. In this context, an essential machine component of a five axis machine tool, the turn-swivel table, serves as the demonstration object for the verification process. Therefore, a turn-swivel table test bench as well as an experimental set-up to measure the beam propagation were developed and are described in the paper. In addition to the empirical investigation, a simulative approach of the described types of experimental examination is presented. Concluding, it is shown that the method and a good understanding of the two core aspects, the thermo-elastic machine behavior and the laser beam path, as well as their combination helps designers to minimize the loss of precision in the early stages of the design phase.

Keywords: additive manufacturing, laser beam machining, machine tool, thermal effects

Procedia PDF Downloads 264

Authors: Thirasak Panyaphirawat, Pairoj Sapsmarnwong, Teeratas Pornyungyuen

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This paper presents the findings of an experimental investigation of important machining parameters for the horizontal boring tool modified to mouth with a horizontal lathe machine to bore an overlength workpiece. In order to verify a usability of a modified tool, design of experiment based on Taguchi method is performed. The parameters investigated are spindle speed, feed rate, depth of cut and length of workpiece. Taguchi L9 orthogonal array is selected for four factors three level parameters in order to minimize surface roughness (Ra and Rz) of S45C steel tubes. Signal to noise ratio analysis and analysis of variance (ANOVA) is performed to study an effect of said parameters and to optimize the machine setting for best surface finish. The controlled factors with most effect are depth of cut, spindle speed, length of workpiece, and feed rate in order. The confirmation test is performed to test the optimal setting obtained from Taguchi method and the result is satisfactory.

Keywords: design of experiment, Taguchi design, optimization, analysis of variance, machining parameters, horizontal boring tool

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Authors: Cecilia Amponsem-Boateng, Timothy B. Oppong, Haiyan Yang, Guangcai Duan

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The human palm has been identified as one of the richest habitats for human microbial accommodation making hand hygiene essential to primary prevention of infection. Since the hand is in constant contact with fomites which have been proven to be mostly contaminated, building hand hygiene habits is essential for the prevention of infection. This research was conducted to assess the hand hygiene habits of Ghanaian youths in Accra. This study used a survey as a quantitative method of research. The findings of the study revealed that out of the 254 participants who fully answered the questionnaire, 22% had the habit of washing their hands after outings while only 51.6% had the habit of washing their hands after using the bathroom. However, about 60% of the participants said they sometimes ate with their hands while 28.9% had the habit of eating with the hand very often, a situation that put them at risk of infection from their hands since some participants had poor handwashing habits; prompting the need for continuous education on hand hygiene.

Keywords: hand hygiene, hand hygiene habit, hand washing, hand sanitizer use

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Authors: T. A. Sakr, Hanaa E. Abd-El-Mottaleb

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Outrigger-braced wall systems are commonly used to provide high rise buildings with the required lateral stiffness for wind and earthquake resistance. The existence of outriggers adds to the stiffness and strength of walls as reported by several studies. The effects of different parameters on the elasto-plastic dynamic behavior of outrigger-braced wall systems to earthquakes are investigated in this study. Parameters investigated include outrigger stiffness, concrete strength, and reinforcement arrangement as the main design parameters in wall design. In addition to being significant to the wall behavior, such parameters may lead to the change of failure mode and the delay of crack propagation and consequently failure as the wall is excited by earthquakes. Bi-linear stress-strain relation for concrete with limited tensile strength and truss members with bi-linear stress-strain relation for reinforcement were used in the finite element analysis of the problem. The famous earthquake record, El-Centro, 1940 is used in the study. Emphasis was given to the lateral drift, normal stresses and crack pattern as behavior controlling determinants. Results indicated significant effect of the studied parameters such that stiffer outrigger, higher grade concrete and concentrating the reinforcement at wall edges enhance the behavior of the system. Concrete stresses and cracking behavior are sigbificantly enhanced while lesser drift improvements are observed.

Keywords: outrigger, shear wall, earthquake, nonlinear

Procedia PDF Downloads 283

Authors: Hind Bouami

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Handling life-critical systems complexity requires to be equipped with appropriate technology and the right human agents’ functions such as knowledge, experience, and competence in problem’s prevention and solving. Human agents are involved in the management and control of human-machine system’s performance. Documenting human agent’s situation awareness is crucial to support human-machine designers’ decision-making. Knowledge about risks, critical parameters and factors that can impact and threaten automation system’s performance should be collected using preventive and retrospective approaches. This paper aims to document operators’ situation awareness through the analysis of automated organizations’ feedback. The analysis of automated hospital pharmacies feedbacks helps to identify and control critical parameters influencing human machine interaction in order to enhance system’s performance and security. Our human machine system evaluation approach has been deployed in Macon hospital center’s pharmacy which is equipped with automated drug dispensing systems since 2015. Automation’s specifications are related to technical aspects, human-machine interaction, and human aspects. The evaluation of drug delivery automation performance in Macon hospital center has shown that the performance of the automated activity depends on the performance of the automated solution chosen, and also on the control of systemic factors. In fact, 80.95% of automation specification related to the chosen Sinteco’s automated solution is met. The performance of the chosen automated solution is involved in 28.38% of automation specifications performance in Macon hospital center. The remaining systemic parameters involved in automation specifications performance need to be controlled.

Keywords: life-critical systems, situation awareness, human-machine interaction, decision-making

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Authors: Yi Huang, Clemens Guehmann

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In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system.

Keywords: asynchronous machine, extended Kalman filter, resistance, simulation, temperature estimation, thermal model

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Authors: Rehan Mohammad

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Background: Improved Water, Sanitation and Hygiene (WASH) practices in schools ensure children’s health, well-being and cognitive performance. In India under various WASH interventions in schools, teachers, and other staff make every possible effort to educate children about personal hygiene, sanitation practices and harms of open defecation. However, once children get back to their families, they see other practicing inappropriate WASH behaviors, and they consequently start following them. This show disconnect between school behavior and family behavior, which needs to be bridged to achieve desired WASH outcomes. Aims and Objectives: The aim of this study is to assess the factors causing disconnect of WASH-related behaviors between school and the family of children. It also suggests behavior change interventions to bridge the gap. Methodology: The present study has chosen a mixed- method approach. Both quantitative and qualitative methods of data collection have been used in the present study. The purposive sampling for data collection has been chosen. The data have been collected from 20% children in each age group of 04-08 years and 09-12 years spread over three primary schools and 20% of households to which they belong to which is spread over three slum communities in south district of Delhi. Results: The present study shows that despite of several behavior change interventions at school level, children still practice inappropriate WASH behaviors due to disconnect between school and family behaviors. These behaviors show variation from one age group to another. The inappropriate WASH behaviors being practiced by children include open defecation, wrong disposal of garbage, not keeping personal hygiene, not practicing hand washing practices during critical junctures and not washing fruits and vegetables before eating. The present study has highlighted that 80% of children in the age group of 04-08 years still practice inappropriate WASH behaviors when they go back to their families after school whereas, this percentage has reduced to 40% in case of children in the age group 09-12 years. Present study uncovers association between school and family teaching which creates a huge gap between WASH-related behavioral practices. The study has established that children learn and de-learn the WASH behaviors due to the evident disconnect between behavior change interventions at schools and household level. The study has also made it clear that children understand the significance of appropriate WASH practices but owing to the disconnect the behaviors remain unsettled. The study proposes several behavior change interventions to sync the behaviors of children at school and family level to ensure children’s health, well-being and cognitive performance.

Keywords: behavioral interventions, child health, family behavior, school behavior, WASH

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Authors: M. H. Belahssen, S. Benramache

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This paper presents corrosion behavior of alloy 42CrMo4 steel nitrided by plasma. Different samples nitrided were tested. The corrosion behavior was evaluated by electrochemical impedance spectroscopy and the tests were carried out in acid chloride solution 1M. The best corrosion protection was observed for nitrided samples. The aim of this work is to compare equivalents circuits corresponding to Nyquist curves simulated and experimental and select who gives best results of impedance parameters with lowest error.

Keywords: pasma nitriding, steel, alloy 42CrMo4, elecrochemistry, corrosion behavior

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Authors: B. Engel, Sara Salman Hassan Al-Maeeni

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Human consumption of the Earth's resources increases the need for a sustainable development as an important ecological, social, and economic theme. Re-engineering of machine tools, in terms of design and failure analysis, is defined as steps performed on an obsolete machine to return it to a new machine with the warranty that matches the customer requirement. To understand the future fatigue behavior of the used machine components, it is important to investigate the possible causes of machine parts failure through design, surface, and material inspections. In this study, the failure modes of the shaft of the rotary draw bending machine are inspected. Furthermore, stress and deflection analysis of the shaft subjected to combined torsion and bending loads are carried out by an analytical method and compared with a finite element analysis method. The theoretical fatigue strength, correction factors, and fatigue life sustained by the shaft before damaged are estimated by creating a stress-cycle (S-N) diagram. In conclusion, it is seen that the shaft can work in the second life, but it needs some surface treatments to increase the reliability and fatigue life.

Keywords: failure analysis, fatigue life, FEM analysis, shaft, stress analysis

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Authors: Kaouther Selmi, Alaeddine Sridi, Mohamed Bouallegue, Kais Bouallegue

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Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology.

Keywords: neural networks, neuron, dendrites, heart behavior, ECG

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Authors: Kohei Yamamoto, Jae-Ho Kim, Susumu Yonezawa

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The surface of polyethylene terephthalate (PET) was modified with fluorine gas at 25 ℃ and 100 Torr for one h. Moreover, the effect of ethanol washing on surface modification was investigated in this study. The surface roughness of the fluorinated and washed PET samples was approximately six times larger than that (0.6 nm) of the untreated thing. The results of Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy showed that the bonds such as -C=O and -C-Hx derived from raw PET decreased and were converted into fluorinated bonds such as -CFx after surface fluorination. Even after washing with ethanol, the fluorinated bonds stably existed on the surface. These fluorinated bonds showed higher electronegativity according to the zeta potential results. The negative surface charges were increased by washing the ethanol, and it caused to increase in the number of polar groups such as -CHF- and -C-Fx. The fluorinated and washed surface of PET could promote the adsorption of Ag+ ions in AgNO₃ solution because of the increased surface roughness and the negatively charged surface.

Keywords: Ag+ ions adsorption, polyethylene terephthalate, surface fluorination, zeta potential

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Authors: Ahmad Saleem

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Ethereum is the second largest crypto currency in blockchain ecosystem. Along with standard transactions, it supports smart contracts and NFT’s. Current research trends are focused on analyzing the overall structure of the network its growth and behavior. Ethereum addresses are anonymous and can be created on fly. The nature of Ethereum network and addresses make it hard to predict their behavior. The activity period of an ethereum address is not much analyzed. Using machine learning we can make early prediction about the disposability of the address. In this paper we analyzed the lifetime of the addresses. We also identified and predicted the disposable addresses using machine learning models and compared the results.

Keywords: blockchain, Ethereum, cryptocurrency, prediction

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Authors: Ehsan Mehryaar, Seyed Armin Motahari Tabari

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Coarse-grained soils are known and commonly used in a wide range of geotechnical projects, including high earth dams or embankments for their high shear strength. The most important engineering property of these soils is friction angle which represents the interlocking between soil particles and can be applied widely in designing and constructing these earth structures. Friction angle and dilative behavior of coarse-grained soils can be estimated from empirical correlations with in-situ testing and physical properties of the soil or measured directly in the laboratory performing direct shear or triaxial tests. Unfortunately, large-scale testing is difficult, challenging, and expensive and is not possible in most soil mechanic laboratories. So, it is common to remove the large particles and do the tests, which cannot be counted as an exact estimation of the parameters and behavior of the original soil. This paper describes a new methodology to simulate particles grading distribution of a well-graded gravel sample to a smaller scale sample as it can be tested in an ordinary direct shear apparatus to estimate the stress-strain behavior, friction angle, and dilative behavior of the original coarse-grained soil considering its confining pressure, and relative density using a machine learning method. A total number of 72 direct shear tests are performed in 6 different sizes, 3 different confining pressures, and 4 different relative densities. Multivariate Adaptive Regression Spline (MARS) technique was used to develop an equation in order to predict shear strength and dilative behavior based on the size distribution of coarse-grained soil particles. Also, an uncertainty analysis was performed in order to examine the reliability of the proposed equation.

Keywords: MARS, coarse-grained soil, shear strength, uncertainty analysis

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Authors: Stepan Grebeniuk, Ersi Hodo, Henri Ruotsalainen, Paul Tavolato

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Substation security plays an important role in the power delivery system. During the past years, there has been an increase in number of attacks on automation networks of the substations. In spite of that, there hasn’t been enough focus dedicated to the protection of such networks. Aiming to design a specialized anomaly detection system based on machine learning, in this paper we will discuss the IEC 60870-5-104 protocol that is used for communication between substation and control station and focus on the simulation of the substation traffic. Firstly, we will simulate the communication between substation slave and server. Secondly, we will compare the system's normal behavior and its behavior under the attack, in order to extract the right features which will be needed for building an anomaly detection system. Lastly, based on the features we will suggest the anomaly detection system for the asynchronous protocol IEC 60870-5-104.

Keywords: Anomaly detection, IEC-60870-5-104, Machine learning, Man-in-the-Middle attacks, Substation security

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Authors: Laura Gonzalez, Santiago Benavides, Martha Elena Londono, Ana Elisa Casas, Adriana Restrepo-Osorio

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Cotton products are sensitive to microorganisms due to its ability to retain moisture, which might cause change into the coloration, mechanical properties reduction or foul odor generation; consequently, this represents risks to the health of users. Nowadays, have been carried out researches to give antibacterial properties to textiles using different strategies, which included the use of silver nanoparticles (AgNPs). The antibacterial behavior can be affected by laundering process reducing its effectiveness. In the other way, the environmental impact generated for the synthetic antibacterial agents has motivated to seek new and more ecological ways for produce AgNPs. The aims of this work are to determine the antibacterial activity of cotton fabric functionalized with green (G) and commercial (C) AgNPs after twenty washing cycles, also to evaluate morphological and color changes. A plain weave cotton fabric suitable for dyeing and two AgNPs solutions were use. C a commercial product and G produced using an ecological method, both solutions with 0.5 mM concentration were impregnated on cotton fabric without stabilizer, at a liquor to fabric ratio of 1:20 in constant agitation during 30min and then dried at 70 °C by 10 min. After that the samples were subjected to twenty washing cycles using phosphate-free detergent simulated on agitated flask at 150 rpm, then were centrifuged and dried on a tumble. The samples were characterized using Kirby-Bauer test determine antibacterial activity against E. coli y S. aureus microorganisms, the results were registered by photographs establishing the inhibition halo before and after the washing cycles, the tests were conducted in triplicate. Scanning electron microscope (SEM) was used to observe the morphologies of cotton fabric and treated samples. The color changes of cotton fabrics in relation to the untreated samples were obtained by spectrophotometer analysis. The images, reveals the presence of inhibition halo in the samples treated with C and G AgNPs solutions, even after twenty washing cycles, which indicated a good antibacterial activity and washing durability, with a tendency to better results against to S. aureus bacteria. The presence of AgNPs on the surface of cotton fiber and morphological changes were observed through SEM, after and before washing cycles. The own color of the cotton fiber has been significantly altered with both antibacterial solutions. According to the colorimetric results, the samples treated with C lead to yellowing while the samples modified with G to red yellowing Cotton fabrics treated AgNPs C and G from 0.5 mM solutions exhibited excellent antimicrobial activity against E. coli and S. aureus with good laundering durability effects. The surface of the cotton fibers was modified with the presence of AgNPs C and G due to the presence of NPs and its agglomerates. There are significant changes in the natural color of cotton fabric due to deposition of AgNPs C and G which were maintained after laundering process.

Keywords: antibacterial property, cotton fabric, fastness to wash, Kirby-Bauer test, silver nanoparticles

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Authors: Khaled M. EL-Naggar

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Synchronizing and damping torque coefficients of a synchronous machine can give a quite clear picture for machine behavior during transients. These coefficients are used as a power system transient stability measurement. In this paper, a crow search optimization algorithm is presented and implemented to study the power system stability during transients. The algorithm makes use of the machine responses to perform the stability study in time domain. The problem is formulated as a dynamic estimation problem. An objective function that minimizes the error square in the estimated coefficients is designed. The method is tested using practical system with different study cases. Results are reported and a thorough discussion is presented. The study illustrates that the proposed method can estimate the stability coefficients for the critical stable cases where other methods may fail. The tests proved that the proposed tool is an accurate and reliable tool for estimating the machine coefficients for assessment of power system stability.

Keywords: optimization, estimation, synchronous, machine, crow search

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Authors: Oladimeji Adetona Adeyeye, Emmanuel Rotimi Sadiku, Oluwaseun Olayinka Adeyeye

Abstract:

In order to resuscitate soybean production and post-harvest processing especially, in term of threshing, there is need to develop an affordable threshing machine which will reduce drudgery associated with manual soybean threshing. Soybean thresher was fabricated and evaluated at Institute of Agricultural Research and Training IAR&T Apata Ibadan. The machine component includes; hopper, threshing unit, shaker, cleaning unit and the seed outlet, all working together to achieve the main objective of threshing and cleaning. TGX1835 - 10E variety was used for evaluation because of its high resistance to pests, rust and pustules. The final moisture content of the used sample was about 15%. The sample was weighed and introduced into the machine. The parameters evaluated includes moisture content, threshing efficiency, cleaning efficiency, machine capacity and speed. The threshing efficiency and capacity are 74% and 65.9kg/hr respectively. All materials used were sourced locally which makes the cost of production of the machine extremely cheaper than the imported soybean thresher.

Keywords: efficiency, machine capacity, speed, soybean, threshing

Procedia PDF Downloads 485

Authors: Allouache Nadia

Abstract:

Solid adsorption cooling machines have been extensively studied recently. They constitute very attractive solutions recover important amount of industrial waste heat medium temperature and to use renewable energy sources such as solar energy. The development of the technology of these machines can be carried out by experimental studies and by mathematical modelisation. This last method allows saving time and money because it is suppler to use to simulate the variation of different parameters. The adsorption cooling machines consist essentially of an evaporator, a condenser and a reactor (object of this work) containing a porous medium, which is in our case the activated carbon reacting by adsorption with ammoniac. The principle can be described as follows: When the adsorbent (at temperature T) is in exclusive contact with vapour of adsorbate (at pressure P), an amount of adsorbate is trapped inside the micro-pores in an almost liquid state. This adsorbed mass m, is a function of T and P according to a divariant equilibrium m=f (T,P). Moreover, at constant pressure, m decreases as T increases, and at constant adsorbed mass P increases with T. This makes it possible to imagine an ideal refrigerating cycle consisting of a period of heating/desorption/condensation followed by a period of cooling/adsorption/evaporation. Effect of key parameters on the machine performances are analysed and discussed.

Keywords: activated carbon-ammoniac pair, effect of key parameters, numerical modeling, solar cooling machine

Procedia PDF Downloads 254

Authors: I Lamri, L Arabet, M. Hidjeb

Abstract:

The direct shear test was used to determine the shear strength parameters C and Ø of a series of samples with different cement content. Samples stabilized with a certain percentage of cement showed a substantial gain in compressive strength and a significant increase in shear strength parameters. C and Ø. The laboratory equipment used in UCS tests consisted of a conventional 102mm diameter sample triaxial loading machine. Beyond 4% cement content a very important increase in shear strength was observed. It can be deduced from a comparative study of shear strength of soil samples with 4%, 7%, and 10% cement with sample containing 2 %, that the sample with a 4% cement content showed 90% increase in shear strength while those with 7% and 10% showed an increase of around 13 and 21 fold.

Keywords: cement, compression strength, shear stress, cohesion, angle of internal friction

Procedia PDF Downloads 488