Search results for: multi-phase induction machine

Authors: K. Kouzi

Abstract:

In order to have stable and high performance of direct torque and flux control (DTFC) of double star induction motor drive (DSIM), proper on-line adaptation of the stator resistance is very important. This is inevitably due to the variation of the stator resistance during operating conditions, which introduces error in estimated flux position and the magnitude of the stator flux. Error in the estimated stator flux deteriorates the performance of the DTFC drive. Also, the effect of error in estimation is very important especially at low speed. Due to this, our aim is to overcome the sensitivity of the DTFC to the stator resistance variation by proposing on-line fuzzy estimation stator resistance. The fuzzy estimation method is based on an on-line stator resistance correction through the variations of the stator current estimation error and its variations. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of the suggested algorithm control is to avoid the drive instability that may occur in certain situations and ensure the tracking of the actual stator resistance. The validity of the technique and the improvement of the whole system performance are proved by the results.

Keywords: direct torque control, dual stator induction motor, Fuzzy Logic estimation, stator resistance adaptation

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Authors: P. Charvin, R. Bourrou, F. Lemont, C. Lafon, A. Russello

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For nuclear waste treatment and confinement, a specific IN-CAN melting module based on low-frequency induction heating have been designed. The frequency of 50Hz has been chosen to improve penetration length through metal. In this design, the liquid metal, strongly stirred by electromagnetic effects, presents shape of a dome caused by strong Laplace forces developing in the bulk of bath. Because of a lower density, the glass phase is located above the metal phase and is heated and stirred by metal through interface. Electric parameters (Intensity, frequency) give precious information about metal load and composition (resistivity of alloy) through impedance modification. Then, power supply can be adapted to energy transfer efficiency for suitable process supervision. Modeling of this system allows prediction of metal dome shape (in agreement with experimental measurement with a specific device), glass and metal velocity, heat and motion transfer through interface. MHD modeling is achieved with COMSOL and Fluent. First, a simplified model is used to obtain the shape of the metal dome. Then the shape is fixed to calculate the fluid flow and the thermal part.

Keywords: electromagnetic stirring, induction heating, interface modeling, metal load

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Authors: Senol Dogan, Gunay Karli

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Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Bachir Belmadani, Rachid Taleb, Zinelaabidine Boudjema, Adil Yahdou

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Carrier-based methods have been used widely for switching of multilevel inverters due to their simplicity, flexibility and reduced computational requirements compared to space vector modulation (SVM). This paper focuses on Multicarrier Sinusoidal Pulse Width Modulation (MCSPWM) strategy for the three phase Five-Level Flying Capacitor Inverter (5LFCI). The inverter is simulated for Induction Motor (IM) load and Total Harmonic Distortion (THD) for output waveforms is observed for different controlling schemes.

Keywords: flying capacitor inverter, multicarrier sinusoidal pulse width modulation, space vector modulation, total harmonic distortion, induction motor

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Authors: M. Abdur Rashid Sarkar, Riffat Mahmud

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Boiling heat transfer plays a crucial role in cooling nuclear reactor for safe electricity generation. A two phase flow is susceptible to thermal-hydrodynamic instabilities, which may cause flow oscillations of constant amplitude or diverging amplitude. These oscillations may induce boiling crisis, disturb control systems, or cause mechanical damage. Based on their mechanisms, various types of instabilities can be classified for a nuclear reactor. From a practical engineering point of view one of the major design difficulties in dealing with multiphase flow is that the mass, momentum, and energy transfer rates and processes may be quite sensitive to the geometric configuration of the heat transfer surface. Moreover, the flow within each phase or component will clearly depend on that geometric configuration. The complexity of this two-way coupling presents a major challenge in the study of multiphase flows and there is much that remains to be done. Yet, the parametric effects on flow instability such as the effect of aspect ratio, pressure drop, channel length, its orientation inlet subcooling and surface roughness etc. have been analyzed. Another frequently occurring instability, known as the Kelvin–Helmholtz instability has been briefly reviewed. Various analytical techniques for predicting parametric effect on the instability are analyzed in terms of their applicability and accuracy.

Keywords: two phase flows, boiling crisis, thermal-hydrodynamic instabilities, water cooled nuclear reactors, kelvin–helmholtz instability

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Authors: Elyas Akbari, Jafar Hasani, Newsha Dehestani, Mohammad Khaleghi, Alireza Moradi

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The aim of the present study was to assess the effect of emotional experience induction in the mirror neurons systems (MNS) activity with regard to the spectrum of depressive symptoms. For this purpose, at first stage, 449 students of Kharazmi University of Tehran were selected randomly and completed the second version of the Beck Depression Inventory (BDI-II). Then, 36 students with standard Z-score equal or above +1.5 and equal or equal or below -1.5 were selected to construct two groups of high and low spectrum of depressive symptoms. In the next stage, the basic activity of MNS was recorded (mu wave) before presenting the positive and negative emotional video clips by Electroencephalography (EEG) technique. The findings related to emotion induction (neutral, negative and positive emotion) demonstrated that the activity of recorded mirror neuron areas had a significant difference between the depressive and non-depressive groups. These findings suggest that probably processing of negative emotions in depressive individuals is due to the idea that the mirror neurons in motor cortex matched up the activity of cognitive regions with the person’s schema. Considering the results of the present study, it could be said that the MNS provides a substrate where emotional disorders can be studied and evaluated.

Keywords: emotional experiences, mirror neurons, depressive symptoms, negative and positive emotion

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Authors: Li Ji, Kaiwei Chu, Shibo Kuang, Aibing Yu

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Hydrocyclones are widely used to classify particles by size in industries such as mineral processing and chemical processing. The particles to be handled usually have a broad range of size distributions and sometimes density distributions, which has to be properly considered, causing challenges in the modelling of hydrocyclone. The combined approach of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) offers convenience to model particle size/density distribution. However, its direct application to hydrocyclones is computationally prohibitive because there are billions of particles involved. In this work, a CFD-DEM model with the concept of the coarse-grained (CG) model is developed to model the solid-fluid flow in a hydrocyclone. The DEM is used to model the motion of discrete particles by applying Newton’s laws of motion. Here, a particle assembly containing a certain number of particles with same properties is treated as one CG particle. The CFD is used to model the liquid flow by numerically solving the local-averaged Navier-Stokes equations facilitated with the Volume of Fluid (VOF) model to capture air-core. The results are analyzed in terms of fluid and solid flow structures, and particle-fluid, particle-particle and particle-wall interaction forces. Furthermore, the calculated separation performance is compared with the measurements. The results obtained from the present study indicate that this approach can offer an alternative way to examine the flow and performance of hydrocyclones

Keywords: computational fluid dynamics, discrete element method, hydrocyclone, multiphase flow

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Authors: Shan-Ying Wu, Sheng-Hui Lan, Xi-Zhang Lin, Ih-Jen Su, Ting-Fen Tsai, Chia-Jui Yen, Tsung-Hsueh Lu, Fu-Wen Liang, Huey-Jen Su, Chun-Li Su, Hsiao-Sheng Liu

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In hepatocelluar carcinoma (HCC), dysregulated expression of cyclin D1 and impaired autophagy has been reported separately. However, the relationship between them has not been explored. In this study, we demonstrated that autophagy was inversely correlated with cyclin D1 expression in 147 paired HCC patient specimens. HCC specimen with highly expression of cyclin D1 shows correlation with poor overall survival rate. Furthermore, induction of autophagy by amiodarone (antiarrhythmic drug) in Hep 3B cells, cyclin D1 was recruited into autophagosomes demonstrated by immune-gold labeling of cyclin D1 after extraction of autophagosomes. We further demonstrated that autophagy suppresses Hep 3B cell proliferation, and further analysis revealed that cell cycle was arrested at G1 phase. The interaction between LC3 (maker of autophagy) and cyclin D1 was increased after autophagy induction. In addition, ubiquitinated-cyclin D1 was also increased after autophagy induction, which is selectively degraded by autophagosome through binding with SQSTM1/p62 (an adaptor protein). In vivo study showed that amiodarone induced autophagy suppresses liver tumor formation in xenograft mouse and orthotopic rat model through decreasing cyclin D1 expression and inhibition of cell proliferation. Altogether, we reveal a novel mechanism that ubiquitinated cyclin D1 degraded by autophagic pathway by p62 and amiodarone is a promising drug for targeting cyclin D1 in liver cancer therapy.

Keywords: autophagy, cyclin D1, hepatocellular carcinoma, amiodarone

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Authors: Seyedmilad Kazemisangdehi, Seyedmehdi Kazemisangdehi

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Flux switching permanent magnet machines are considered for wide range of applications because of their outstanding merits including high torque/power densities, high efficiency, simple and robust rotor structure. Therefore, several topologies have been proposed like the PM exited flux switching machine, hybrid excited flux switching type, and so on. Recently, a novel laminated segmented rotor flux switching permanent magnet machine was introduced. It features flux barriers on rotor structure to enhance the performances of machine including torque ripple reduction and also torque and efficiency improvements at the same time. This is while, the design of barriers was not optimized by the authors. Therefore, in this paper three coefficients regarding the position of the barriers are considered for optimization. The effect of each coefficient on the performance of this machine is investigated by finite element method and finally an optimized design of flux barriers based on these three coefficients is proposed from different points of view including electromagnetic torque maximization and cogging torque/torque ripple minimization. At optimum design from maximum developed torque aspect, this machine generates 0.65 Nm torque higher than that of the not-optimized design with an almost 0.4 % improvement in efficiency.

Keywords: finite element analysis, FSPM, laminated segmented rotor flux switching permanent magnet machine, optimization

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: H. Zou, B. Wang

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Thermal distortion of the machine tool plays a critical role in its machining accuracy. This study investigates the thermal performance of a high-precision machine frame with future-oriented mineral casting components. A thermo-mechanical finite element model (FEM) was established to evaluate the thermal behavior of the frame under environmental thermal fluctuations. The validity of the presented FEM model was confirmed experimentally by a series of laser interferometer tests. Good agreement between numerical and experimental results demonstrates that the proposed model can accurately predict the thermal deformation of the frame with thermo-mechanical coupling effect. The results also show that keeping the workshop in thermally stable conditions is crucial for improving the machine accuracy of the system with large scale components. The goal of this paper is to investigate the feasibility of innovative mineral casting material applied in high-precision drilling machine and to provide a strategy for machine tool industry seeking a perfect substitute for classic frame materials such as cast iron and granite.

Keywords: thermo-mechanical model, finite element method, laser interferometer, mineral casting frame

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Authors: Jiahe Ru, Yan Pang, Zhaomiao Liu

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Necking processes of the Janus droplet generation in the cross-junction microchannels are experimentally and theoretically investigated. The two dispersed phases that are simultaneously shear by continuous phases are liquid paraffin wax and 100cs silicone oil, in which 80% glycerin aqueous solution is used as continuous phases. According to the variation of minimum neck width and thinning rate, the necking process is divided into two stages, including the two-dimensional extrusion and the three-dimensional extrusion. In the two-dimensional extrusion stage, the evolutions of the tip extension length for the two discrete phases begin with the same trend, and then the length of liquid paraffin is larger than silicone oil. The upper and lower neck interface profiles in Janus necking process are asymmetrical when the tip extension velocity of paraffin oil is greater than that of silicone oil. In the three-dimensional extrusion stage, the neck of the liquid paraffin lags behind that of the silicone oil because of the higher surface tension, and finally, the necking fracture position gradually synchronizes. When the Janus droplets pinch off, the interfacial tension becomes positive to drive the neck thinning. The interface coupling of the three phases can cause asymmetric necking of the neck interface, which affects the necking time and, ultimately, the droplet volume. This paper mainly investigates the thinning dynamics of the liquid-liquid interface in confined microchannels. The revealed results could help to enhance the physical understanding of the droplet generation phenomenon.

Keywords: neck interface, interface coupling, janus droplets, multiphase flow

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Authors: Marzena Tokarewicz, Malgorzata Gradzka-Dahlke

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High-entropy alloys (HEAs) have gained significant attention due to their great potential as functional and structural materials. HEAs have very good mechanical properties (in particular, alloys based on CoCrNi). They also show the ability to maintain their strength at high temperatures, which is extremely important in some applications. AlCoCrFeNi alloy is one of the most studied high-entropy alloys. Scientists often study the effect of changing the aluminum content in this alloy because it causes significant changes in phase presence and microstructure and consequently affects its hardness, ductility, and other properties. Research conducted by the authors also investigates the effect of aluminium content in AlₓCoCrFeNi alloy on its microstructure and mechanical properties. AlₓCoCrFeNi alloys were prepared by vacuum induction melting. The obtained samples were examined for chemical composition, microstructure, and microhardness. The three-point bending method was carried out to determine the bending strength, bending modulus, and conventional bending yield strength. The obtained results confirm the influence of aluminum content on the properties of AlₓCoCrFeNi alloy. Most studies on AlₓCoCrFeNi alloy focus on the determination of mechanical properties in compression or tension, much less in bending. The achieved results provide valuable information on the bending properties of AlₓCoCrFeNi alloy and lead to interesting conclusions.

Keywords: bending properties, high-entropy alloys, induction melting, microstructure

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Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer Experience Management, Paper Machine , Value Chain Management, Risk Analysis

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Authors: K. Julia Rose Mary, Victor Arokia Doss

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Electrical and chemical stimulations up-regulate phosphorylaion of CREB, a transcriptional factor that induces its target gene production for memory consolidation and Late Long-Term Potentiation (L-LTP) in CA1 region of the hippocampus. L-LTP requires complex interactions among second-messenger signaling cascade molecules such as cAMP, CAMKII, CAMKIV, MAPK, RSK, PKA, all of which converge to phosphorylate CREB which along with CBP induces the transcription of target genes involved in memory consolidation. A differential equation based model for L-LTP representing stimulus-mediated activation of downstream mediators which confirms the steep, supralinear stimulus-response effects of activation and inhibition was used. The same was extended to accommodate the inhibitory effect of the Inducible cAMP Early Repressor (ICER). ICER is the natural inducible CREB antagonist represses CRE-Mediated gene transcription involved in long-term plasticity for learning and memory. After verifying the sensitivity and robustness of the model, we had simulated it with various empirical levels of repressor concentration to analyse their effect on the gene induction. The model appears to predict the regulatory dynamics of repression on the L-LTP and agrees with the experimental values. The flux data obtained in the simulations demonstrate various aspects of equilibrium between the gene induction and repression.

Keywords: CREB, L-LTP, mathematical modeling, simulation

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Authors: Jenq-Shyong Chen, Ben-Fong Yu

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CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system.

Keywords: auto-tuning, CNC parameters, machining mode, high speed, high accuracy, high surface quality

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Authors: Mostafa A. Amer, I. A. El-Samra, I. I. Abou-ElSeoud, S. M. El-Abd, N. K. Shawertamimi

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Tomato Fusarium wilt disease caused by Fusarium oxysporum f. sp. Lycopercisi (FOL) is considered one of the most destructive diseases in Egypt. Effect of some biotic inducers such as Bacillus megaterium var. phosphaticum, Glomus intraradices and Glomus macrocarpum at seven different mixed treatments, was tested for their ability to induce resistance in tomato plants against the disease. According to pathogenicity tests, all the tested isolates of FOL showed wilt symptoms on both of the tested cultivars; however, they considerably varied in percentages of disease incidence (DI) and disease severity (DS). Castle Rock was more susceptible than Peto 86, which was relatively resistant. Pretreatment of both cultivars, under greenhouse conditions, with the tested biotic inducers alone or in combination with each other's, significantly increased the induction of chitinase, β-1,3-glucanase, peroxidase, and polyphenoloxidase and reduced disease incidence and severity, compared with untreated noninoculated (C1) and untreated inoculated (C2) controls. Application of a combination of BMP, with GI and GM was the most effective in increasing the induction rated of the tested enzymes, compared with the other treatments. Induction of enzymes in most of the tested bioinducers treatments gradually increased, attaining maximum values after 48 or/and 72 hrs after challenging with FOL, then gradually declined. GI was the least effective bioinducer.

Keywords: F. oxysporum f. sp. lycopersici, defense enzymes, induced systemic resistance, ISR, B. megaterium var. phosphaticum, G. macrocarpum, G. intraradices

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Authors: Sergei Voychuk

Abstract:

Introduction: Adaptive response (AR) is a manifestation of radiation hormesis, which deal with the radiation resistance that may be increased with the pretreatment with small doses of radiation. In the current study, we evaluated the potency of radiofrequency EMF to induce the AR mechanisms and to increase a resistance to UV light. Methods: Saccharomyces cerevisiae yeast strains, which were created to study induction of mutagenesis and recombination, were used in the study. The strains have mutations in rad2 and rad54 genes, responsible for DNA repair: nucleotide excision repair (PG-61), postreplication repair (PG-80) and mitotic (crossover) recombination (T2). An induction of mutation and recombination are revealed due to the formation of red colonies on agar plates. The PG-61 and T2 are UV sensitive strains, while PG-80 is sensitive to ionizing radiation. Extremely high frequency electromagnetic field (EHF-EMF) was used. The irradiation was performed in floating mode and frequency changed during exposure from 57 GHz to 62 GHz. The power of irradiation was 100 mkW, and duration of exposure was 10 and 30 min. Treatment was performed at RT and then cells were stored at 28° C during 1 h without any exposure but after that they were treated with UV light (254nm) for 20 sec (strain T2) and 120 sec (strain PG-61 and PG-80). Cell viability and quantity of red colonies were determined after 5 days of cultivation on agar plates. Results: It was determined that EHF-EMF caused 10-20% decrease of viability of T2 and PG-61 strains, while UV showed twice stronger effect (30-70%). EHF-EMF pretreatment increased T2 resistance to UV, and decreased it in PG-61. The PG-80 strain was insensitive to EHF-EMF and no AR effect was determined for this strain. It was not marked any induction of red colonies formation in T2 and PG-80 strain after EHF or UV exposure. The quantity of red colonies was 2 times more in PG-61 strain after EHF-EMF treatment and at least 300 times more after UV exposure. The pretreatment of PG-61 with EHF-EMF caused at least twice increase of viability and consequent decrease of amount of red colonies. Conclusion: EHF-EMF may induce AR in yeast cells and increase their viability under UV treatment.

Keywords: Saccharomyces cerevisiae, EHF-EMF, UV light, adaptive response

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

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Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

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Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

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Authors: Revanth Rallapalli

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Manually dispensing solids and powders can be difficult as it requires gradually pour and check the amount on the scale to be dispensed. Current systems are manual and non-continuous in nature and are user-dependent and difficult to control powder dispensation. Recurrent dosing of powdered medicines in precise amounts quickly and accurately has been an all-time challenge. Various new powder dispensing mechanisms are being designed to overcome these challenges. A battery-operated screw conveyor mechanism is being innovated to overcome the above problems faced. These inventions are numerically evaluated at the concept development level by employing Computational Fluid Dynamics (CFD) of gas-solids multiphase flow systems. CFD has been very helpful in development of such devices saving time and money by reducing the number of prototypes and testing. Furthermore, this paper describes a simulation of powder dispensation from the trocar’s end by considering the powder as secondary flow in air, is simulated by using the technique called Dense Discrete Phase Model incorporated with Kinetic Theory of Granular Flow (DDPM-KTGF). By considering the volume fraction of powder as 50%, the transportation of powder from the inlet side to trocar’s end side is done by rotation of the screw conveyor. Thus, the performance is calculated for a 1-sec time frame in an unsteady computation manner. This methodology will help designers in developing design concepts to improve the dispensation and also at the effective area within a quick turnaround time frame.

Keywords: DDPM-KTGF, gas-solids multiphase flow, screw conveyor, Unsteady

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Authors: Youn-Hwan Kim, Hae-Joong Kim, Jae-Won Moon

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This paper introduces the results of the study on the development of accelerated life test methods for the motor used in machine tools. In recent years, as well as efficiency for motors, there is a growing need for research on life expectancy of motors. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. This paper describes the equipment development procedure for the highly accelerated life test (HALT) of the 12kW three-phase squirrel-cage induction motors (SCIMs). After the test, the lifetime analysis was carried out and it is compared with the bearing life expectancy by ISO 281.

Keywords: acceleration coefficient, bearing, HALT, life expectancy, motor

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Wenfeng Xie

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Bulk/hull cavitation evolution induced by an underwater explosion (UNDEX) near a free surface (bulk) or a deformable structure (hull) is numerically investigated using a multiphase compressible fluid solver coupled with a one-fluid cavitation model. A series of two-dimensional computations is conducted with varying material elasticity and surface curvature. Results suggest that material elasticity and surface curvature influence the peak pressures generated from UNDEX shock and cavitation collapse, as well as the bulk/hull cavitation regions near the surface. Results also show that such effects can be different for bulk cavitation generated from UNDEX-free surface interaction and for hull cavitation generated from UNDEX-structure interaction. More importantly, results demonstrate that shock wave focusing caused by a concave solid surface can lead to a larger cavitation region and thus intensify the cavitation reload. The findings can be linked to the strength and the direction of reflected waves from the structural surface and reflected waves from the expanding bubble surface, which are functions of material elasticity and surface curvature. Shockwave focusing effects are also observed for axisymmetric simulations, but the strength of the pressure contours for the axisymmetric simulations is less than those for the 2D simulations due to the difference between the initial shock energy. The current method is limited to two-dimensional or axisymmetric applications. Moreover, the thermal effects are neglected and the liquid is not allowed to sustain tension in the cavitation model.

Keywords: cavitation, UNDEX, fluid-structure interaction, multiphase

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

Abstract:

Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

Abstract:

Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Valeriu Mihai But, Sorana Daniela Bolboacă, Adriana Elena Bulboacă

Abstract:

The nutraceutical compound Curcumin (Curcuma longa L.) is known for its anti-inflammatory, anti-cancer, and antioxidant effects. This study aimed to evaluate the antioxidative and analgesic effects of Curcumin (CC) compared to Tramadol (T) in chemical-induced nociceptive pain in rats. Thirty-five rats were randomly divided into five groups of seven rats each and were treated as follows: C group (control group): treated with saline solution 0.9%, (1 ml, i.p. administration), ethanoic acid (EA) group: pretreated with saline solution 0.9% - 30 min before EA nociceptive pain induction, (1 ml, i.p. administration), T group: pretreated with Tramadol, 10 mg/kg body weight (bw), i.p. administration - 30 min before EA nociceptive pain induction, CC1-group: pretreated with 1 mg/100g bw Curcumin i.p. administration - 2 days before EA pain induction and CC2-group: pretreated with Curcumin 2 mg/100g bw i.p. administration - 2 days before EA nociceptive pain induction. The following oxidative stress parameters were assessed: malondialdehyde (MDA), nitric oxide (NOx), total oxidative status (TOS), total antioxidative capacity (TAC), and thiol (Th). The antalgic activity was measured by the ethanoic acid writhing test. Treatment with Curcumin, both 1 mg/100g bw, and 2 mg/100g bw, showed significant differences as compared with the control group (p<0.001) regarding malondialdehyde (MDA), nitric oxide (NOx), and total oxidative status (TOS) oxidative biomarkers. Pretreatment with 2 mg/100g bw of Curcumin presented a significant decrease in MDA values compared with Tramadol (p<0.001). The TAC significantly increased in pretreatment with Curcumin compared with group control. (p<0.001) The nociceptive response to EA was significantly reduced in Curcumin and Tramadol groups. Treatment with Curcumin at a higher concentration was more effective. In an experimental pain model, this study demonstrates an important antioxidant and antinociceptive activity of Curcumin comparable with Tramadol treatment.

Keywords: curcumin, nociception, oxidative stress, pain

Procedia PDF Downloads 82