Search results for: multi-phase induction machine

Authors: Golnush Masghati-Amoli, Paul Chin

Abstract:

Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

Procedia PDF Downloads 133

Authors: Hanifeh Imanian, Morteza Kolahdoozan

Abstract:

The purpose of this study is to present a mathematical phrase for calculating the dispersion rate of spilled oil in water column under non-breaking waves. In this regard, a multiphase numerical model is applied for which waves and oil phase were computed concurrently, and accuracy of its hydraulic calculations have been proven. More than 200 various scenarios of oil spilling in wave waters were simulated using the multiphase numerical model and its outcome were collected in a database. The recorded results were investigated to identify the major parameters affected vertical oil dispersion and finally 6 parameters were identified as main independent factors. Furthermore, some statistical tests were conducted to identify any relationship between the dependent variable (dispersed oil mass in the water column) and independent variables (water wave specifications containing height, length and wave period and spilled oil characteristics including density, viscosity and spilled oil mass). Finally, a mathematical-statistical relationship is proposed to predict dispersed oil in marine waters. To verify the proposed relationship, a laboratory example available in the literature was selected. Oil mass rate penetrated in water body computed by statistical regression was in accordance with experimental data was predicted. On this occasion, it was necessary to verify the proposed mathematical phrase. In a selected laboratory case available in the literature, mass oil rate penetrated in water body computed by suggested regression. Results showed good agreement with experimental data. The validated mathematical-statistical phrase is a useful tool for oil dispersion prediction in oil spill events in marine areas.

Keywords: dispersion, marine environment, mathematical-statistical relationship, oil spill

Procedia PDF Downloads 233

Authors: Xiaobo Xi, Ruihong Zhang

Abstract:

At present, the orchard ditching and fertilizing technology has a series of problems, such as easy tree roots damage, high energy consumption and uneven fertilizing. In this paper, a gas explosion subsoiling and fertilizer injection machine was designed, which used high pressure gas to shock soil body and then injected fertilizer. The drill pipe mechanism with pneumatic chipping hammer excitation and hydraulic assistance was designed to drill the soil. The operation of gas and liquid fertilizer supply was controlled by PLC system. The 3D model of the whole machine was established by using SolidWorks software. The machine prototype was produced, and field experiments were carried out. The results showed that soil fractures were created and diffused by gas explosion, and the subsoiling effect radius reached 40 cm under the condition of 0.8 MPa gas pressure and 30 cm drilling depth. What’s more, the work efficiency is 0.048 hm²/h at least. This machine could meet the agronomic requirements of orchard, garden and city greening fertilization, and the tree roots were not easily damaged and the fertilizer evenly distributed, which was conducive to nutrient absorption of root growth.

Keywords: gas explosion subsoiling, fertigation, pneumatic chipping hammer exciting, soil compaction

Procedia PDF Downloads 209

Authors: Yan Yan, Erhong Sun, Lin Peng, Xuchun Ye

Abstract:

Aims: The present study aimed to explore Intensive Care Unit(ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance(ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction.

Keywords: mental workload(MWL), nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China

Procedia PDF Downloads 104

Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

Procedia PDF Downloads 203

Authors: Amina Unis, Samah S. El Basateeny, Noha A. H. Nassef

Abstract:

Introduction: Acute Renal Failure (ARF) is one of the most common problems encountered in hospitalized critically ill patients. In recent years great effort has been focused on the introduction of herbal medicine as a novel therapeutic agent for prevention of ARF. Hence, the current study was designed to investigate the effect of Green Coffee Bean Extract (GCBE) on gentamicin induced ARF in rats. Methods: The study was conducted on 60 male rats divided into six equal groups. Group 1 served as normal control group and GCBE was administered for 7 days at a dose of 20 mg/kg/day in group 2 and 40 mg/kg/day in group 3 to test the effect of GCBE on normal kidneys. ARF was induced by a daily intraperitoneal injection of gentamicin (80 mg/kg) for 7 days in group 4 (model group), group 5 (GCBE 20 mg/kg/day) and group 6 (GCBE 20 mg/kg/day). All rats were sacrificed after 7 days and blood was withdrawn for kidney function tests. Kidneys were removed for determination of renal oxidative stress markers and histopathological examination. Results: The present study showed that rats that received oral GCBE for 7 days without induction of ARF showed no significant change in all the assessed parameters in comparison to the normal control group, while rats in the groups that received oral GCBE for 7 days with induction of ARF showed a significant improvement in kidney functions tests (decrease in serum urea, serum creatinine, and blood urea nitrogen) when compared to the ARF model group. Moreover, there was significant amelioration in renal oxidative stress markers (renal malondialdehyde, renal superoxide dismutase) and renal histopathological changes in the GCBE treated groups along induction of ARF when compared to ARF model group. The most significant improvement was reported in the group where GCBE was administered for 7 days in a dose 40 mg/kg/day, along with induction of ARF. Conclusion: GCBE has a potential role in ameliorating renal damage involved in ARF mostly through its antioxidant effect.

Keywords: green coffee bean extract, gentamicin, acute renal failure, pharmacology

Procedia PDF Downloads 292

Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

Abstract:

In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

Procedia PDF Downloads 115

Authors: Song Zhiyong, Zhao Bo, Du Li, Wang Wei

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Aircraft structures generally have complex surface. Because of constantly switching postures of motion axis, five-axis CNC machine’s composite stiffness changes during CNC machining. It gives rise to different amplitude of vibration of processing system, which further leads to the different effects on surface waviness. In order to provide a solution for this problem, we take the “S” shape test specimen’s CNC machining for the object, through calculate the five axis CNC machine’s composite stiffness and establish vibration model, we analysis of the influence mechanism between vibration amplitude and surface waviness. Through carry out the surface quality measurement experiments, verify the validity and accuracy of the theoretical analysis. This paper’s research results provide a theoretical basis for surface waviness control.

Keywords: five axis CNC machine, “S” shape test specimen, composite stiffness, surface waviness

Procedia PDF Downloads 390

Authors: Chothmal, Basant Agarwal

Abstract:

Sentiment analysis means to classify a given review document into positive or negative polar document. Sentiment analysis research has been increased tremendously in recent times due to its large number of applications in the industry and academia. Sentiment analysis models can be used to determine the opinion of the user towards any entity or product. E-commerce companies can use sentiment analysis model to improve their products on the basis of users’ opinion. In this paper, we propose a new One-class Support Vector Machine (One-class SVM) based sentiment analysis model for movie review documents. In the proposed approach, we initially extract features from one class of documents, and further test the given documents with the one-class SVM model if a given new test document lies in the model or it is an outlier. Experimental results show the effectiveness of the proposed sentiment analysis model.

Keywords: feature selection methods, machine learning, NB, one-class SVM, sentiment analysis, support vector machine

Procedia PDF Downloads 517

Authors: Amir Taghizadeh Vahed, Adithya Thaduri

Abstract:

Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

Procedia PDF Downloads 218

Authors: Ezio Bassi, Francesco Vercesi, Francesco Benzi

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The aim of this study is to evaluate the potentiality of synchronous reluctance motors for lift systems by also evaluating the vibroacoustic behaviour of the motor. Two types of synchronous machines are designed, analysed, and compared with an equivalent induction motor, which is the more common solution in such gearbox applications. The machines' performance are further improved with optimization procedures based on multiobjective optimization genetic algorithm (MOGA). The difference between the two synchronous motors consists in the rotor geometry; a symmetric and an asymmetric rotor design were investigated. The evaluation of the vibroacoustic performance has been conducted with a multi-variable model and finite element software taking into account electromagnetic, mechanical, and thermal features of the motor, therefore carrying out a multi-physics analysis of the electrical machine.

Keywords: synchronous reluctance motor, vibro-acoustic, lift systems, genetic algorithm

Procedia PDF Downloads 178

Authors: Ali Moharrek, Hosein Mobli, Ali Jafari, Ahmad Tabataee Far

Abstract:

Biomass is a material generally produced by plants living on soil or water and their derivatives. The remains of agricultural and forest products contain biomass which is changeable into fuel. Besides, you can obtain biogas and ethanol from the charcoal produced from biomass through specific actions. this technology was designed for as a useful Native Fuel and Technology in Energy disasters Management Due to the sudden interruption of the flow of heat energy One of the problems confronted by mankind in the future is the limitations of fossil energy which necessitates production of new energies such as biomass. In order to produce biomass from the remains of the plants, different methods shall be applied considering factors like cost of production, production technology, area of requirement, speed of work easy utilization, ect. In this article we are focusing on designing a biomass briquetting portable machine. The speed of installation of the machine on a tractor is estimated as 80 MF 258. Screw press is used in designing this machine. The needed power for running this machine which is estimated as 17.4 kW is provided by the power axis of tractor. The pressing speed of the machine is considered to be 375 RPM Finally the physical and mechanical properties of the product were compared with utilized material which resulted in appropriate outcomes. This machine is designed for Gathering Raw materials of the ground by Head Section. During delivering the raw materials to Briquetting section, they Crushed, Milled & Pre Heated in Transmission section. This machine is a Combine Portable Tractor unit machine and can use all type of Agriculture, Forest & Livestock Animals Resides as Raw material to make Bio fuel. The Briquetting Section was manufactured and it successfully made bio fuel of Sawdust. Also this machine made a biofuel with Ethanol of sugarcane Wastes. This Machine is using P.T.O power source for Briquetting and Hydraulic Power Source for Pre Processing of Row Materials.

Keywords: biomass, briquette, screw press, sawdust, animal wastes, portable, tractors

Procedia PDF Downloads 316

Authors: Dipranjan Laha, Parimal Karmakar

Abstract:

Metal oxide nanoparticles are well known to generate oxidative stress and deregulate normal cellular activities. Among these, transition metals copper oxide nanoparticles (CuO NPs) are more compelling than others and able to modulate different cellular responses. In this work, we have synthesized and characterized CuO NPs by various biophysical methods. These CuO NPs (~30 nm) induce autophagy in human breast cancer cell line, MCF7 in a time and dose-dependent manner. Cellular autophagy was tested by MDC staining, induction of green fluorescent protein light chain 3 (GFP-LC3B) foci by confocal microscopy, transfection of pBABE-puro mCherry-EGFP-LC3B plasmid and western blotting of autophagy marker proteins LC3B, beclin1, and ATG5. Further, inhibition of autophagy by 3-Methyladenine (3-MA) decreased LD50 doses of CuO NPs. Such cell death was associated with the induction of apoptosis as revealed by FACS analysis, cleavage of PARP, dephosphorylation of Bad and increased cleavage product of caspase3. siRNA-mediated inhibition of autophagy-related gene beclin1 also demonstrated similar results. Finally, induction of apoptosis by 3-MA in CuO NPs treated cells were observed by TEM. This study indicates that CuO NPs are a potent inducer of autophagy which may be a cellular defense against the CuO NPs mediated toxicity and inhibition of autophagy switches the cellular response into apoptosis. A combination of CuO NPs with the autophagy inhibitor is essential to induce apoptosis in breast cancer cells. Acknowledgments: The authors would like to acknowledge for financial support for this research work to the Department of Biotechnology (No. BT/PR14661/NNT/28/494/2010), Government of India.

Keywords: nanoparticle, autophagy, apoptosis, siRNA-mediated inhibition

Procedia PDF Downloads 440

Authors: Carlos Oliveira Santiago Filho, Ciro Egoavil, Eduardo Oliveira, Jéferson Galdino, Moises Galileu, Tiago Oliveira Correa

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Transfer of Electrical Energy through resonant inductive magnetic coupling is demonstrated experimentally in a system containing coil primary for transmission and secondary reception. The topology used in the prototype of the Class-E amplifier, has been identified as optimal for power transfer applications. Characteristic of the inductor and the load are defined by the requirements of the resonant inductive system. The frequency limitation the of circuit restricts unloaded “Q-Factor”, quality factor of the coils and thus the link efficiency. With a suitable circuit, copper coil unloaded Q-Factors of over 1,000 can be achieved in the low Mhz region, enabling a cost-effective high Q coil assembly. The circuit is capable system capable of transmitting energy with direct current to load efficiency above 60% at 2 Mhz.

Keywords: magnetic induction, transfer of electrical energy, magnetic coupling, Q-Factor

Procedia PDF Downloads 518

Authors: Sam Khozama, Ali M. Mayya

Abstract:

Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 161

Authors: Jin Uk Han, Hye Won Han, Hyo Lim Kang, Tae An Kim, Seung Ho Han

Abstract:

Direct-drive generators for large-scale wind turbine, which are divided into AFPM(Axial Flux Permanent Magnet) and RFPM(Radial Flux Permanent Magnet) type machine, have attracted interest because of a higher energy density in comparison with gear train type generators. Each type of the machines provides distinguishable geometrical features such as narrow width with a large diameter for the AFPM-type machine and wide width with a certain diameter for the RFPM-type machine. When the AFPM-type machine is applied, an increase of electric power production through a multi-stage arrangement in axial direction is easily achieved. On the other hand, the RFPM-type machine can be applied by using its geometric feature of wide width. In this study, a hybrid two-stage direct-drive generator for 6.2MW class wind turbine was proposed, in which the two-stage AFPM-type machine for 5 MW was composed of two models arranged in axial direction with a hollow shape topology of the rotor with annular disc, the stator and the main shaft mounted on coupled slew bearings. In addition, the RFPM-type machine for 1.2MW was installed at the empty space of the rotor. Analytic results obtained from an electro-magnetic and structural interaction analysis showed that the structural weight of the proposed hybrid two-stage direct-drive generator can be achieved as 155tonf in a condition satisfying the requirements of structural behaviors such as allowable air-gap clearance and strength. Therefore, it was sure that the 6.2MW hybrid two-stage direct-drive generator is competitive than conventional generators. (NRF grant funded by the Korea government MEST, No. 2017R1A2B4005405).

Keywords: AFPM-type machine, direct-drive generator, electro-magnetic analysis, large-scale wind turbine, RFPM-type machine

Procedia PDF Downloads 167

Authors: Joel Weymouth, Ramesh K. Karne, Alexander L. Wijesinha

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Bare Machine Internet is part of the Bare Machine Computing (BMC) paradigm. It is used in programming application ns to run directly on a device. It is software that runs directly against the hardware using CPU, Memory, and I/O. The software application runs without an Operating System and resident mass storage. An important part of the BMC paradigm is the Bare Machine Internet. It utilizes an Application Development model software that interfaces directly with the hardware on a network server and file server. Because it is “bare,” it is a powerful teaching and research tool that can readily display the internals of the network protocols, software, and hardware of the applications running on the Bare Server. It was also demonstrated that the bare server was accessible by laptop and by smartphone/android. The purpose was to show the further practicality of Bare Internet in Computer Engineering and Computer Science Education and Research. It was also to show that an undergraduate student could take advantage of a bare server with any device and any browser at any release version connected to the internet. This paper presents the Bare Web Server as an educational tool. We will discuss possible applications of this paradigm.

Keywords: bare machine computing, online research, network technology, visualizing network internals

Procedia PDF Downloads 172

Authors: S. R. M. Krishna, R. Santosh Kumar, V. Kamakshi Prasad

Abstract:

Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). DALYs for a disease is the sum of years of life lost due to premature mortality (YLLs) + No of years of healthy life lost due to disability (YLDs). The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DAL, YLD, YLL

Procedia PDF Downloads 36

Authors: Oliver Commichau, Richard Krimm, Bernd-Arno Behrens

Abstract:

A growing demand on the production rate of modern presses leads to higher stroke rates. Commonly used material feeding devices for presses like grippers and roll-feeding systems can only achieve high stroke rates along with high gripper forces, to avoid stick-slip. These forces are limited by the sensibility of the surfaces of the workpieces. Stick-slip leads to scratches on the surface and false positioning of the workpiece. In this paper, a new contactless feeding device is presented, which develops higher feeding force without damaging the surface of the workpiece through gripping forces. It is based on the principle of the linear induction motor. A primary part creates a magnetic field and induces eddy currents in the electrically conductive material. A Lorentz-Force applies to the workpiece in feeding direction as a mutual reaction between the eddy-currents and the magnetic induction. In this study, the FEA model of this approach is shown. The calculation of this model was used to identify the influence of various design parameters on the performance of the feeder and thus showing the promising capabilities and limits of this technology. In order to validate the study, a prototype of the feeding device has been built. An experimental setup was used to measure pulling forces and placement accuracy of the experimental feeder in order to give an outlook of a potential industrial application of this approach.

Keywords: conductive material, contactless feeding, linear induction, Lorentz-Force

Procedia PDF Downloads 179

Authors: Yan Yan, Erhong Sun, Lin Peng, Xuchun Ye

Abstract:

Aims: The present study aimed to explore Intensive Care Unit (ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance (ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction.

Keywords: mental workload, nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China

Procedia PDF Downloads 69

Authors: Nima Mahmoudi, Hamzeh Khazaei

Abstract:

Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

Procedia PDF Downloads 179

Authors: Mohamud Ali Ibrahim, Ali Bayat, Ali Bolat

Abstract:

Defoliant spraying is an important link in the mechanized cotton harvest because adequate and uniform spraying can improve defoliation quality and reduce cotton trash content. In defoliant application, application volume and spraying technology are extremely important. In this study, the effectiveness of defoliant application to cotton plant that has come to harvest with two different application volumes and three different types of nozzles with a standard field crop sprayer was determined. Experiments were carried in two phases as field area trials and laboratory analysis. Application rates were 250 l/ha and 400 L/ha, and spraying nozzles were (1) Standard flat fan nozzle (TP8006), (2) Air induction nozzle (AI 11002-VS), and (3) Dual Pattern nozzle (AI307003VP). A tracer (BSF) and defoliant were applied to mature cotton with approximately 60% open bolls and samplings for BSF deposition and spray coverage on the cotton plant were done at two plant height (upper layer, lower layer) of plant. Before and after spraying, bolls open and leaves rate on cotton plants were calculated, and filter papers were used to detect BSF deposition, and water sensitive papers (WSP) were used to measure the coverage rate of spraying methods used. Spectrofluorophotometer was used to detect the amount of tracer deposition on targets, and an image process computer programme was used to measure coverage rate on WSP. In analysis, conclusions showed that air induction nozzle (AI 11002-VS) achieved better results than the dual pattern and standard flat fan nozzles in terms of higher depositions, coverages, and leaf defoliations, and boll opening rates. AI nozzles operating at 250 L/ha application rate provide the highest deposition and coverage rate on applications of the defoliant; in addition, BSF as an indicator of the defoliant used reached on leaf beneath in merely this spray nozzle. After defoliation boll opening rate was 85% on the 7th and 12th days after spraying and falling rate of leaves was 76% at application rate of 250 L/ha with air induction (AI1102) nozzle.

Keywords: cotton defoliant, air induction nozzle, dual pattern nozzle, standard flat fan nozzle, coverage rate, spray deposition, boll opening rate, leaves falling rate

Procedia PDF Downloads 195

Authors: S. Apprich, F. Wulle, A. Lechler, A. Pott, A. Verl

Abstract:

Large-scale machine tools for the manufacturing of large work pieces, e.g. blades, casings or gears for wind turbines, feature pose-dependent dynamic behavior. Small structural damping coefficients lead to long decay times for structural vibrations that have negative impacts on the production process. Typically, these vibrations are handled by increasing the stiffness of the structure by adding mass. That is counterproductive to the needs of sustainable manufacturing as it leads to higher resource consumption both in material and in energy. Recent research activities have led to higher resource efficiency by radical mass reduction that rely on control-integrated active vibration avoidance and damping methods. These control methods depend on information describing the dynamic behavior of the controlled machine tools in order to tune the avoidance or reduction method parameters according to the current state of the machine. The paper presents the appearance, consequences and challenges of the pose-dependent dynamic behavior of lightweight large-scale machine tool structures in production. The paper starts with the theoretical introduction of the challenges of lightweight machine tool structures resulting from reduced stiffness. The statement of the pose-dependent dynamic behavior is corroborated by the results of the experimental modal analysis of a lightweight test structure. Afterwards, the consequences of the pose-dependent dynamic behavior of lightweight machine tool structures for the use of active control and vibration reduction methods are explained. Based on the state of the art on pose-dependent dynamic machine tool models and the modal investigation of an FE-model of the lightweight test structure, the criteria for a pose-dependent model for use in vibration reduction are derived. The description of the approach for a general pose-dependent model of the dynamic behavior of large lightweight machine tools that provides the necessary input to the aforementioned vibration avoidance and reduction methods to properly tackle machine vibrations is the outlook of the paper.

Keywords: dynamic behavior, lightweight, machine tool, pose-dependency

Procedia PDF Downloads 459

Authors: M. Rajasekar, K. Suresh

Abstract:

Diosmin is a flavonoid, most abundantly found in many citrus fruits. As a flavonoid, it possesses a multitude of biological activities including anti-hyperglycemic, anti-lipid peroxidative, anti-inflammatory, antioxidant, and anti-mutagenic properties. At this point, we established the anti-proliferative and apoptosis-inducing activities of diosmin in Hep-2 and KB cells. Diosmin has cytotoxic effects through inhibiting cellular proliferation of Hep-2 and KB cells, which leads to the induction of apoptosis, as apparent by an increase in the fraction of cells in the sub-G1phase of the cell cycle. Results exposed that inhibition of cell proliferation is associated with regulation of the Interleukins/STAT pathway. In addition, Diosmin treatment with Hep-2 and KB cells actively stimulated reactive oxygen species (ROS) and mitochondrial membrane depolarization. And also an imbalance in the Bax/Bcl-2 ratio triggered the caspase cascade and shifting the balance in favor of apoptosis. These observations conclude that Diosmin induce apoptosis via Interleukins /STAT-mediated pathway.

Keywords: diosmin, apoptosis, antioxidant, STAT pathway

Procedia PDF Downloads 328

Authors: Glaoui Hachemi

Abstract:

In this paper, a scheme based on multi-input multi output Fuzzy Sliding Mode control (MIMO-FSMC) for linear speed regulation of winding system is proposed. Once the uncoupled model of the winding system was obtained, a smooth control function with a threshold was selected to indicate how far away the case was from the sliding surface. nevertheless, this control function depends closely on the higher bound of the uncertainties, which generates overlap. So, this size has to be chosen with broad care to obtain high performances. Usually, the upper bound of uncertainties is difficult to know before motor operation, so, a Fuzzy Sliding Mode controller is investigated to resolve this problem, a simple Fuzzy inference mechanism is used to decrease the chattering phenomenon by simple adjustments. A simulation study is achieved and that the indicate fuzzy sliding mode controllers have great potential for use as an alternative to the conventional sliding mode control.

Keywords: Winding system, induction machine, Mechanical tension, Proportional-integral (PI), sliding mode control, Fuzzy logic

Procedia PDF Downloads 95

Authors: Sreenesh Shashidharan, Umesh Gaikwad

Abstract:

QI or 'Chee' which is an interface standard for inductive electrical power transfer over distances of up to 4 cm (1.6 inches). The Qi system comprises a power transmission pad and a compatible receiver in a portable device which is placed on top of the power transmission pad, which charges using the principle of electromagnetic induction. An alternating current is passed through the transmitter coil, generating a magnetic field. This, in turn, induces a voltage in the receiver coil; this can be used to power a mobile device or charge a battery. The efficiency of the power transfer depends on the coupling (k) between the inductors and their quality (Q) The coupling is determined by the distance between the inductors (z) and the relative size (D2 /D). The coupling is further determined by the shape of the coils and the angle between them. If the receiver coil is at a certain distance to the transmitter coil, only a fraction of the magnetic flux, which is generated by the transmitter coil, penetrates the receiver coil and contributes to the power transmission. The more flux reaches the receiver, the better the coils are coupled.

Keywords: inductive electric power, electromagnetic induction, magnetic flux, coupling

Procedia PDF Downloads 732

Authors: Jakkireddy Sravani

Abstract:

Introduction: The maintenance of hemodynamic stability is of pivotal importance in supratentorial surgeries. Anesthesia for supratentorial tumors requires an understanding of localized or generalized rising ICP, regulation, and maintenance of intracerebral perfusion, and avoidance of secondary systemic ischemic insults. We aimed to compare the effects of the combination of ketamine and propofol with propofol alone when used as an induction and maintenance anesthetic agent during supratentorial tumors. Methodology: This prospective, randomized, double-blinded controlled study was conducted at AIIMS Raipur after obtaining the institute Ethics Committee approval (1212/IEC-AIIMSRPR/2022 dated 15/10/2022), CTRI/2023/01/049298 registration and written informed consent. Fifty-two supratentorial tumor patients posted for craniotomy and excision were included in the study. The patients were randomized into two groups. One group received a combination of ketamine and propofol, and the other group received propofol for induction and maintenance of anesthesia. Intraoperative hemodynamic stability and quality of brain relaxation were studied in both groups. Statistical analysis and technique: An MS Excel spreadsheet program was used to code and record the data. Data analysis was done using IBM Corp SPSS v23. The independent sample "t" test was applied for continuously dispersed data when two groups were compared, the chi-square test for categorical data, and the Wilcoxon test for not normally distributed data. Results: The patients were comparable in terms of demographic profile, duration of the surgery, and intraoperative input-output status. The trends in BIS over time were similar between the two groups (p-value = 1.00). Intraoperative hemodynamics (SBP, DBP, MAP) were better maintained in the ketamine and propofol combination group during induction and maintenance (p-value < 0.01). The quality of brain relaxation was comparable between the two groups (p-value = 0.364). Conclusion: Ketamine and propofol combination for the induction and maintenance of anesthesia was associated with superior hemodynamic stability, required fewer vasopressors during excision of supratentorial tumors, provided adequate brain relaxation, and some degree of neuroprotection compared to propofol alone.

Keywords: supratentorial tumors, hemodynamic stability, brain relaxation, ketamine, propofol

Procedia PDF Downloads 25

Authors: Alireza Barari, Ahmad Abdi

Abstract:

The aim of this study was to determine the effects of the effects of six weeks endurance training and Aloe Vera on cyclooxygenase 2 (COX-2) and VEGF levels in mice with breast cancer. For this purpose, 35 rats were randomly divided into 5 groups: control (healthy), control (cancer), training (cancer), Aloe Vera (cancer) and Aloe Vera + training (cancer). Induction of breast cancer tumors were done in mice by planting method. The training program includes six weeks of swimming training was done in three sessions per week. Training time from 10 minutes on the first day increased to 60 minutes in second week, and by stabilizing this time, the water flow rate was increased from 7 to 15 liters per minute. 300 mg per kg body weight of Aloe Vera extract was injected into the peritoneal. Sampling was done 48 hours after the last exercise session. K-S test to determine the normality of the data and analysis of variance for repeated measures and Tukey test was used to analyze the data. A significant difference in the p<0.05 accepted. The results showed that induction of cancer cells significantly increased levels of COX-2 in aloe group and VEGF in training and Aloe Vera + training groups. The results suggest that swimming exercise and Aloe Vera can reduce levels of COX-2 and VEGF in mice with breast cancer.The results of this study, Induction of cancer cells significantly increased levels of COX-2 and MMP-9 in the control group compared with the cancer control group. The results suggest that Aloe Vera can probably inhibit the cyclooxygenase pathway and thus production of prostaglandin E2 decrease of arachidonic acid.

Keywords: endurance training, aloe vera, COX-2, VEGF

Procedia PDF Downloads 289

Authors: M. A. García, J. Vinolas, A. Hernando

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Steel is nowadays one of the most important structural materials because of its outstanding mechanical properties. Therefore, in order to look for a sustainable economic model and to optimize the use of extensive resources, new methods to monitor and prevent failure of steel-based facilities are required. The classical mechanical tests, as for instance building tasting, are invasive and destructive. Moreover, for facilities where the steel element is embedded, (as reinforced concrete) these techniques are directly non applicable. Hence, non-invasive monitoring techniques to prevent failure, without altering the structural properties of the elements are required. Among them, electromagnetic methods are particularly suitable for non-invasive inspection of the mechanical state of steel-based elements. The magnetoelastic coupling effects induce a modification of the electromagnetic properties of an element upon applied stress. Since most steels are ferromagnetic because of their large Fe content, it is possible to inspect their structure and state in a non-invasive way. We present here a distinct electromagnetic method for contactless evaluation of internal stress in steel-based elements. In particular, this method relies on measuring the magnetic induction between two coils with the steel specimen in between them. We found that the alteration of electromagnetic properties of the steel specimen induced by applied stress-induced changes in the induction allowed us to detect stress well below half of the elastic limit of the material. Hence, it represents an outstanding non-invasive method to prevent failure in steel-based facilities. We here describe the theoretical model, present experimental results to validate it and finally we show a practical application for detection of stress and inhomogeneities in train railways.

Keywords: magnetoelastic, magnetic induction, mechanical stress, steel

Procedia PDF Downloads 50

Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

Abstract:

Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

Procedia PDF Downloads 713