Search results for: genetic algorithm

Authors: N. N. Pandey, Raghvendra Singh, Biju S. Kamlam, Bipin K. Vishwakarma, Preetam Kala

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The rainbow trout (Oncorhynchus mykiss) is an exotic salmonid fish, well known for its fast growth, tremendous ability to thrive in diverse conditions, delicious flesh and hard fighting nature in Europe and other countries. Rainbow trout farming has a great potential for its contribution to the mainstream economy of Himalayan states in India and other temperate countries. These characteristics establish them as one of the most widely introduced and cultured fish across the globe, and its farming is also prominent in the cold water regions of India. Nevertheless, genetic fatigue, slow growth, early maturity, and low productivity are limiting the expansion of trout production. Moreover, farms adjacent to natural streams or other water sources are subject to escape of domesticated rainbow trout into the wild, which is a serious environmental concern as the escaped fish is subject to contaminate and disrupt the receiving ecosystem. A decline in production traits due to early maturity prolongs the culture duration and affects the profit margin of rainbow trout farms in India. A viable strategy that could overcome these farming constraints in large scale operation is the production of triploid fish that are sterile and more heterozygous. For better triploidy induction rate (TR), heat shock at 28°C for 10 minutes and pressure shock 9500 psi pressure for 5 minutes is applied to green eggs with 90-100% of triploidy success and 72-80% survival upto swim-up fry stage. There is 20% better growth in aquaculture with triploids rainbow trout over diploids. As compared to wild diploid fish, larger sized and fitter triploid rainbow trout in natural waters attract to trout anglers, and support the development of recreational fisheries by state fisheries departments without the risk of contaminating existing gene pools and disrupting local fish diversity. Overall, enhancement of productivity in rainbow trout farms and trout production in coldwater regions, development of lucrative trout angling and better ecological management is feasible with triploid rainbow trout.

Keywords: rainbow trout, triploids fish, heat shock, pressure shock, trout angling

Procedia PDF Downloads 116

Authors: Roberto Coppo, Francesca Orso, Daniela Dettori, Elena Quaglino, Lei Nie, Mehran M. Sadeghi, Daniela Taverna

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Malignant melanoma is currently the fifth most common cancer in the white population and it is fatal in its metastatic stage. Several research studies in recent years have provided evidence that cancer initiation and progression are driven by genetic alterations of the tumor and paracrine interactions between tumor and microenvironment. Scattered data show that the Endothelial and Smooth muscle cell-Derived Neuropilin-like molecule (ESDN) controls cell proliferation and movement of stroma and tumor cells. To investigate the role of ESDN in the tumor microenvironment during melanoma progression, murine melanoma cells (B16 or B16-F10) were injected in ESDN knockout mice in order to evaluate how the absence of ESDN in stromal cells could influence melanoma progression. While no effect was found on primary tumor growth, increased cell extravasation and lung metastasis formation was observed in ESDN knockout mice compared to wild type controls. In order to understand how cancer cells cross the endothelial barrier during metastatic dissemination in an ESDN-null microenvironment, structure, and permeability of lung blood vessels were analyzed. Interestingly, ESDN knockout mice showed structurally altered and more permeable vessels compared to wild type animals. Since cell surface molecules mediate the process of tumor cell extravasation, the expression of a panel of extravasation-related ligands and receptors was analyzed. Importantly, modulations of N-cadherin, E-selectin, ICAM-1 and VAP-1 were observed in ESDN knockout endothelial cells, suggesting the presence of a favorable tumor microenvironment which facilitates melanoma cell extravasation and metastasis formation in the absence of ESDN. Furthermore, a potential contribution of immune cells in tumor dissemination was investigated. An increased recruitment of macrophages in the lungs of ESDN knockout mice carrying subcutaneous B16-F10 tumors was found. In conclusion, our data suggest a functional role of ESDN in the tumor microenvironment during melanoma progression and the identification of the mechanisms that regulate tumor cell extravasation could lead to the development of new therapies to reduce metastasis formation.

Keywords: melanoma, tumor microenvironment, extravasation, cell surface molecules

Procedia PDF Downloads 321

Authors: Rabiaa Gammoudi, Najet Rebei, Othman Hasnaoui

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Photovoltaic systems use different techniques for tracking the Maximum Power Point (MPPT) to provide the highest possible power to the load regardless of the climatic conditions variation. In this paper, the proposed method is the Open Circuit (OC) method with sudden and random variations of insolation. The simulation results of the water pumping system controlled by OC method are validated by an experimental experience in real-time using a test bench composed by a centrifugal pump powered by a PVG via a boost chopper for the adaptation between the source and the load. The output of the DC/DC converter supplies the motor pump LOWARA type, assembly by means of a DC/AC inverter. The control part is provided by a computer incorporating a card DS1104 running environment Matlab/Simulink for visualization and data acquisition. These results show clearly the effectiveness of our control with a very good performance. The results obtained show the usefulness of the developed algorithm in solving the problem of degradation of PVG performance depending on the variation of climatic factors with a very good yield.

Keywords: PVWPS (PV Water Pumping System), maximum power point tracking (MPPT), open circuit method (OC), boost converter, DC/AC inverter

Procedia PDF Downloads 440

Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

Procedia PDF Downloads 378

Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

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Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

Procedia PDF Downloads 176

Authors: Linhang Zhu, Hongyu Zhu, Jiahe Liu

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This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control.

Keywords: Manhattan world hypothesis, real-time positioning and mapping, feature matching, loopback detection

Procedia PDF Downloads 49

Authors: Alabi Olufemi, Adeyanju Taiwo, Oloruntoba Oluwasegun, Adeleye Bobola, Alabi Oyekemi

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The pastoral system is a predominant form of cattle production in Nigeria, characterized by extensive grazing on communal lands. However, this system is challenged by various factors, including tick infestation, which significantly affects cattle health and welfare hence this investigation which aims to provide an in-depth understanding of tick infestation in the context of Nigerian pastoral systems, emphasizing its impact on cattle health and welfare. The country harbors a diverse array of tick species that affect cattle. These ticks belong to different genera, including Rhipicephalus, Amblyomma, and Hyalomma, among others. Each species has unique characteristics, life cycles, and host preferences, contributing to the complexity of tick infestation dynamics in pastoral settings. Tick infestation has numerous detrimental effects on cattle health. The direct effects include blood loss, anemia, skin damage due to feeding, and the transmission of pathogens that cause diseases such as anaplasmosis, babesiosis, and theileriosis. Indirectly, tick infestation can lead to reduced productivity, weight loss, and increased susceptibility to other diseases.The welfare of cattle in Nigerian pastoral systems is significantly impacted by tick infestation. Infested cattle often exhibit signs of distress, including restlessness, reduced grazing activity, and altered behavior. Furthermore, the discomfort caused by tick bites can lead to chronic stress, compromising the overall welfare of the animals. Effective tick control is crucial for mitigating the impact of infestation on cattle health and welfare. Strategies such as acaricide application, pasture management, genetic selection for tick resistance cattle, and vaccination against tick-borne diseases are commonly used. Tick infestation presents a significant challenge to cattle production under the pastoral system in Nigeria. It not only impacts cattle health but also compromises their welfare. Addressing the issue of tick infestation requires a multifaceted approach that integrates effective control strategies with sustainable management practices. Further research is needed to develop tailored interventions that account for the unique characteristics of Nigerian pastoral systems, ultimately ensuring the well-being and productivity of cattle in these settings.

Keywords: tick infestation, pastoral system, welfare, cattle

Procedia PDF Downloads 34

Authors: Xiaohua Zou, Yongxin Su

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The reasonable operation of heating, ventilation, and air conditioning (HVAC) is of great significance in improving the security, stability, and economy of power system operation. However, the uncertainty of the operating environment, thermal comfort varies by users and rapid decision-making pose challenges for HVAC demand response optimization. In this regard, this paper proposes a reinforcement learning-based method for HVAC demand response optimization considering few-shot personalized thermal comfort (PTC). First, an HVAC DR optimization framework based on few-shot PTC model and DRL is designed, in which the output of few-shot PTC model is regarded as the input of DRL. Then, a few-shot PTC model that distinguishes between awake and asleep states is established, which has excellent engineering usability. Next, based on soft actor criticism, an HVAC DR optimization algorithm considering the user’s PTC is designed to deal with uncertainty and make decisions rapidly. Experiment results show that the proposed method can efficiently obtain use’s PTC temperature, reduce energy cost while ensuring user’s PTC, and achieve rapid decision-making under uncertainty.

Keywords: HVAC, few-shot personalized thermal comfort, deep reinforcement learning, demand response

Procedia PDF Downloads 66

Authors: N. Boutaghane, F. Z. Tounsi

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Many physical and technological factors degrade the SPECT images, both qualitatively and quantitatively. For this, it is not always put into leading technological advances to improve the performance of tomographic gamma camera in terms of detection, collimation, reconstruction and correction of tomographic images methods. We have to master firstly the choice of various acquisition and reconstruction parameters, accessible to clinical cases and using the attenuation and scatter correction methods to always optimize quality image and minimized to the maximum dose received by the patient. In this work, an evaluation of qualitative and quantitative tomographic images is performed based on the acquisition parameters (counts per projection) and reconstruction parameters (filter type, associated cutoff frequency). In addition, methods for correcting physical effects such as attenuation and scatter degrading the image quality and preventing precise quantitative of the reconstructed slices are also presented. Two approaches of attenuation and scatter correction are implemented: the attenuation correction by CHANG method with a filtered back projection reconstruction algorithm and scatter correction by the subtraction JASZCZAK method. Our results are considered as such recommandation, which permits to determine the origin of the different artifacts observed both in quality control tests and in clinical images.

Keywords: attenuation, scatter, reconstruction filter, image quality, acquisition and reconstruction parameters, SPECT

Procedia PDF Downloads 433

Authors: Rafi Ullah, Shah Muhammad Emaduddin, Taha Jilani

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Vehicle Adhoc Networks (VANET) belongs to a special class of Mobile Adhoc Network (MANET) with high mobility. Network is created by road side vehicles equipped with communication devices like GPS and Wifi etc. Since the environment is highly dynamic due to difference in speed and high mobility of vehicles and weak stability of the network connection, it is a challenging task to design an efficient routing protocol for such an unstable environment. Our proposed algorithm uses heuristic for the calculation of optimal path for routing the packet efficiently in collaboration with several other parameters like geographical location, speed, priority, the distance among the vehicles, communication range, and networks congestion. We have incorporated probabilistic, heuristic and machine learning based approach inconsistency with the relay function of the memory buffer to keep the packet moving towards the destination. These parameters when used in collaboration provide us a very strong and admissible heuristics. We have mathematically proved that the proposed technique is efficient for the routing of packets, especially in a medical emergency situation. These networks can be used for medical emergency, security, entertainment and routing purposes.

Keywords: heuristics routing, intelligent routing, VANET, route optimization

Procedia PDF Downloads 163

Authors: M. Dieb, T. Hodairi

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In the Southwest Libya area, the Palaeozoic deposits are an important petroleum system, with Silurian shale considered a hydrocarbon source rock and Cambro-Ordovician recognized as a good reservoir. The Palaeozoic petroleum system has the greatest potential for conventional and is thought to represent the significant prospect of unconventional petroleum resources in Southwest Libya. Until now, the lateral and vertical heterogeneity of the source rock was not well evaluated, and oil-source correlation is still a matter of debate. One source rock, which is considered the main source potential in Marzuq Basin, was investigated for its uranium contents using gamma-ray logs, rock-eval pyrolysis, and organic petrography for their bulk kinetic characteristics to determine the petroleum potential qualitatively and quantitatively. Thirty source rock samples and fifteen oil samples from the Tannezzuft source rock were analyzed by Rock-Eval Pyrolysis, microscopely investigation, GC, and GC-MS to detect acyclic isoprenoids and aliphatic, aromatic, and NSO biomarkers. Geochemistry tools were applied to screen source and age-significant biomarkers to high-spot genetic relationships. A grating heterogeneity exists among source rock zones from different levels of depth with varying uranium contents according to gamma-ray logs, rock-eval pyrolysis results, and kinetic features. The uranium-rich Tannezzuft Formations (Hot Shales) produce oils and oil-to-gas hydrocarbons based on their richness, kerogen type, and thermal maturity. Biomarker results such as C₂₇, C₂₈, and C₂₉ steranes concentrations and C₂₄ tetracyclic terpane/C₂₉ tricyclic terpane ratios, with sterane and hopane ratios, are considered the most promising biomarker information in differentiating within the Silurian Shale Tannezzuft Formation and in correlating with its expelled oils. The Tannezzuft Hot Shale is considered the main source rock for oil and gas accumulations in the Cambro-Ordovician reservoirs within the Marzuq Basin. Migration of the generated and expelled oil and gas from the Tannezzuft source rock to the reservoirs of the Cambro-Ordovician petroleum system was interpreted to have occurred along vertical and lateral pathways along the faults in the Palaeozoic Strata. The Upper Tannezzuft Formation (cold shale) is considered the primary seal in the Marzuq Basin.

Keywords: heterogeneity, hot shale, kerogen, Silurian, uranium

Procedia PDF Downloads 48

Authors: Aktar-Uz-Zaman, M. Tuhina-Khatun, Mohamed Hanafi Musa

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Three rust diseases: leaf (brown) rust caused by Puccinia triticina Eriks, stripe (yellow) rust caused by Puccinia striiformis West, and stem (black) rust caused by Puccinia graminis f. sp. tritici are economically important diseases of wheat in world wide. Yield loss due to leaf rust is 40% in susceptible cultivars. Yield losses caused by the stem rust pathogens in the mid of 20 century reached 20-30% in Eastern and Central Europe and the most virulent stem rust race Ug99 emerged first in Uganda and after that in Kenya, Ethiopia, Yemen, in the Middle East and South Asia. Yield losses were estimated up to 100%, whereas, up to 80% have been reported in Kenya during 1999. In case of stripe rust, severity level has been recorded 60% - 70% as compared to 100% severity of susceptible check in disease screening nurseries in Kenya. Improvement of resistant varieties or cultivars is the sustainable, economical and environmentally friendly approaches for increasing the global wheat production to suppress the rust diseases. More than 68 leaf rust, 49 stripe rust and 53 stem rust resistance genes have been identified in the global wheat cultivars or varieties using different molecular breeding approaches. Among these, Lr1, Lr9, Lr10, Lr19, Lr21, Lr24, Lr25, Lr28, Lr29, Lr34, Lr35, Lr37, Lr39, Lr47, Lr51, Lr3bg, Lr18, Lr40, Lr46, and Lr50 leaf rust resistance genes have been identified by using molecular, enzymatic and microsatellite markers from African, Asian, European cultivars of hexaploid wheat (Triticum aestivum), durum wheat and diploid wheat species. These genes are located on 20, of the 21 chromosomes of hexaploid wheat. Similarly, Sr1, Sr2, Sr24, and Sr3, Sr31 stem rust resistance genes have been recognized from wheat cultivars of Pakistan, India, Kenya, and Uganda etc. A race of P. striiformis (stripe rust) Yr9, Yr18, and Yr29 was first observed in East Africa, Italy, Pakistan and India wheat cultivars. These stripe rust resistance genes are located on chromosomes 1BL, 4BL, 6AL, 3BS and 6BL in bread wheat cultivars. All these identified resistant genes could be used for notable improvement of susceptible wheat cultivars in the future.

Keywords: hexaploid wheat, resistance genes, rust disease, triticum aestivum

Procedia PDF Downloads 470

Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

Procedia PDF Downloads 112

Authors: M. Maache, R. Bessaih

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In this study, the effect of the magnetic field direction on the free convection heat transfer in a vertical cylinder filled with an Al₂O₃ nanofluid is investigated numerically. The external magnetic field is applied in either direction axial and radial on a cylinder having an aspect ratio H/R0=5, bounded by the top and the bottom disks at temperatures Tc and Th and by an adiabatic side wall. The equations of continuity, Navier Stocks and energy are non-dimensionalized and then discretized by the finite volume method. A computer program based on the SIMPLER algorithm is developed and compared with the numerical results found in the literature. The numerical investigation is carried out for different governing parameters namely: The Hartmann number (Ha=0, 5, 10, …, 40), nanoparticles volume fraction (ϕ=0, 0.025, …,0.1) and Rayleigh number (Ra=103, Ra=104 and Ra=105). The behavior of average Nusselt number, streamlines and temperature contours are illustrated. The results revel that the average Nusselt number increases with an increase of the Rayleigh number but it decreases with an increase in the Hartmann number. Depending on the magnetic field direction and on the values of Hartmann and Rayleigh numbers, an increase of the solid volume fraction may result enhancement or deterioration of the heat transfer performance in the nanofluid.

Keywords: natural convection, nanofluid, magnetic field, vertical cylinder

Procedia PDF Downloads 307

Authors: Esra Ece Var

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Beekeeping plays essential role both in terms of agricultural yields and agricultural economy; they produce honey, wax, royal jelly, apitoxin, pollen, and propolis. Nowadays, these natural products become more importantly suitable and preferable for nutrition, food supplement, medicine, and industry. However, to produce organic honey, majority of the apiaries are located in remote or distant rural areas where utilities such as electricity and Internet network are not available. Additionally, due to colony failures, world honey production decreases year by year despite the increase in the number of beehives. The objective of this paper is to develop a smart beehive monitoring system for apiaries including those that do not have access to Internet network. In this context, temperature and humidity inside the beehive, and ambient temperature were measured with RFID sensors. Control center, where all sensor data was sent and stored at, has a GSM module used to warn the beekeeper via SMS when an anomaly is detected. Simultaneously, using the collected data, an unsupervised machine learning algorithm is used for detecting anomalies and calibrating the warning system. The results show that the smart beehive monitoring system can detect fatal anomalies up to 4 weeks prior to colony loss.

Keywords: beekeeping, smart systems, machine learning, anomaly detection, apiculture

Procedia PDF Downloads 220

Authors: Lan Zhang, Cailing Zhang

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In recent years, many face anti-spoofing algorithms have high detection accuracy when detecting 2D face anti-spoofing or 3D mask face anti-spoofing alone in the field of face anti-spoofing, but their detection performance is greatly reduced in multidimensional and cross-datasets tests. The rPPG method used for face anti-spoofing uses the unique vital information of real face to judge real faces and face anti-spoofing, so rPPG method has strong stability compared with other methods, but its detection rate of 2D face anti-spoofing needs to be improved. Therefore, in this paper, we improve an rPPG(Remote Photoplethysmography) method(MrPPG) for face anti-spoofing which through color space fusion, using the correlation of pulse signals between real face regions and background regions, and introducing the cyclic neural network (LSTM) method to improve accuracy in 2D face anti-spoofing. Meanwhile, the MrPPG also has high accuracy and good stability in face anti-spoofing of multi-dimensional and cross-data datasets. The improved method was validated on Replay-Attack, CASIA-FASD, Siw and HKBU_MARs_V2 datasets, the experimental results show that the performance and stability of the improved algorithm proposed in this paper is superior to many advanced algorithms.

Keywords: face anti-spoofing, face presentation attack detection, remote photoplethysmography, MrPPG

Procedia PDF Downloads 165

Authors: Leonardo A. Ambrosio, Carlos. H. Silva Santos, Ivan E. L. Rodrigues, Ayumi K. de Campos, Leandro A. Machado

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In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.

Keywords: Bessel Beams and Frozen Waves, Generalized Lorenz-Mie Theory, Numerical Methods, optical forces

Procedia PDF Downloads 368

Authors: Masoud Safarishaal

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Detecting the location of pole-to-earth faults is essential for the safe operation of the electrical system of the railroad. This paper aims to use a combination of evolutionary algorithms and neural networks to increase the accuracy of single pole-to-earth fault detection and location on the Tehran railroad power supply system. As a result, the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO) are used to train the neural network to improve the accuracy and convergence of the learning process. Due to the system's nonlinearity, fault detection is an ideal application for the proposed method, where the 600 Hz harmonic ripple method is used in this paper for fault detection. The substations were simulated by considering various situations in feeding the circuit, the transformer, and typical Tehran metro parameters that have developed the silicon rectifier. Required data for the network learning process has been gathered from simulation results. The 600Hz component value will change with the change of the location of a single pole to the earth's fault. Therefore, 600Hz components are used as inputs of the neural network when fault location is the output of the network system. The simulation results show that the proposed methods can accurately predict the fault location.

Keywords: single pole-to-pole fault, Tehran railway, ICA, PSO, artificial neural network

Procedia PDF Downloads 104

Authors: Moti Zwilling, Srečko Natek

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This paper aims to present an innovative predictive analytics analysis in order to find the best combination between two consumers who strive to find their partner or in internet sites. The methodology shown in this paper is based on analysis of consumer preferences and involves data mining and machine learning search techniques. The study is composed of two parts: The first part examines by means of descriptive statistics the correlations between a set of parameters that are taken between man and women where they intent to meet each other through the social media, usually the internet. In this part several hypotheses were examined and statistical analysis were taken place. Results show that there is a strong correlation between the affiliated attributes of man and woman as long as concerned to how they present themselves in a social media such as "Facebook". One interesting issue is the strong desire to develop a serious relationship between most of the respondents. In the second part, the authors used common data mining algorithms to search and classify the most important and effective attributes that affect the response rate of the other side. Results exhibit that personal presentation and education background are found as most affective to achieve a positive attitude to one's profile from the other mate.

Keywords: dating sites, social networks, machine learning, decision trees, data mining

Procedia PDF Downloads 284

Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

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This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: microwave filter, scattering parameter, coupling matrix, intelligent tuning

Procedia PDF Downloads 289

Authors: Muhammad Abdullah, Asma Rashid Butt, Nauman Raza

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Biomagnetic fluids like blood play key role in different applications of medical science and bioengineering. In this paper, the magnetohydrodynamic flow of a viscous fluid with magnetic particles through a cylindrical tube is investigated. The fluid is electrically charged in the presence of a uniform external magnetic field. The movement in the fluid is produced due to the cylindrical tube. Initially, the fluid and tube are at rest and at time t=0⁺, the tube starts to move along its axis. To obtain the mathematical model of flow with fractional derivatives fractional calculus approach is used. The solution of the flow model is obtained by using Laplace transformation. The Simon's numerical algorithm is employed to obtain inverse Laplace transform. The hybrid technique, we are employing has less computational effort as compared to other methods. The numerical calculations have been performed with Mathcad software. As the special cases of our problem, the solution of flow model with ordinary derivatives and flow without magnetic particles has been procured. Finally, the impact of non-integer fractional parameter alpha, Hartmann number Ha, and Reynolds number Re on flow and magnetic particles velocity is analyzed and depicted by graphs.

Keywords: viscous fluid, magnetic particles, fractional calculus, laplace transformation

Procedia PDF Downloads 184

Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

Procedia PDF Downloads 123

Authors: S. Saju, G. Thirugnanam

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In this paper, a medical image watermarking algorithm based on contourlet is proposed. Medical image watermarking is a special subcategory of image watermarking in the sense that images have special requirements. Watermarked medical images should not differ perceptually from their original counterparts because clinical reading of images must not be affected. Watermarking techniques based on wavelet transform are reported in many literatures but robustness and security using contourlet are better when compared to wavelet transform. The main challenge in exploring geometry in images comes from the discrete nature of the data. In this paper, original image is decomposed to two level using contourlet and the watermark is embedded in the resultant sub-bands. Sub-band selection is based on the value of Peak Signal to Noise Ratio (PSNR) that is calculated between watermarked and original image. To extract the watermark, Kernel ICA is used and it has a novel characteristic is that it does not require the transformation process to extract the watermark. Simulation results show that proposed scheme is robust against attacks such as Salt and Pepper noise, Median filtering and rotation. The performance measures like PSNR and Similarity measure are evaluated and compared with Discrete Wavelet Transform (DWT) to prove the robustness of the scheme. Simulations are carried out using Matlab Software.

Keywords: digital watermarking, independent component analysis, wavelet transform, contourlet

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Authors: Amirreza Razzaghipour Sorkhab

Abstract:

One of the most common disorders among humans is hearing impairment. This paper provides an evidence base that recovers understanding of Meniere’s disease and highlights the physical and mental health correlates of the disorder. Meniere's disease is more common in the elderly. The term idiopathic endolymphatic hydrops has been attributed to this disease by some in the previous. Meniere’s disease demonstrations a genetic tendency, and a family history is found in 10% of cases, with an autosomal dominant inheritance pattern. The COCH gene may be one of the hereditary factors contributing to Meniere’s disease, and the possibility of a COCH mutation should be considered in patients with Meniere’s disease symptoms. Should be considered Missense mutations in the COCH gene cause the autosomal dominant sensorineural hearing loss and vestibular disorder. Meniere’s disease is a complex, heterogeneous disorder of the inner ear and that is characterized by episodes of vertigo lasting from minutes to hours, fluctuating sensorineural hearing loss, tinnitus, and aural fullness. The existing evidence supports the suggestion that age and sleep disorder are risk factors for Meniere's disease. Many factors have been reported to precipitate the progress of Menier, including endolymphatic hydrops, immunology, viral infection, inheritance, vestibular migraine, and altered intra-labyrinthine fluid dynamics. Although there is currently no treatment that has a proven helpful effect on hearing levels or on the long-term evolution of the disease, however, in the primary stages, the hearing may improve among attacks, but a permanent hearing loss occurs in the majority of cases. Current publications have proposed a role for the intratympanic use of medicine, mostly aminoglycosides, for the control of vertigo. more than 85% of patients with Meniere's disease are helped by either changes in lifestyle and medical treatment or minimally aggressive surgical procedures such as intratympanic steroid therapy, intratympanic gentamicin therapy, and endolymphatic sac surgery. However, unilateral vestibular extirpation methods (intratympanic gentamicin, vestibular nerve section, or labyrinthectomy) are more predictable but invasive approaches to control the vertigo attacks. Medical therapy aimed at reducing endolymph volume, such as low-sodium diet, diuretic use, is the typical initial treatment.

Keywords: meniere's disease, endolymphatic hydrops, hearing loss, vertigo, tinnitus, COCH gene

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Authors: Myoung Ah Kim, Dong Ho Sin, Chul Gyu Song

Abstract:

Blood oxygen saturation system is a well-established, noninvasive photoplethysmographic method to monitor vital signs. Conventional blood oxygen saturation measurements for the two LED light source is the ambiguity of the oxygen saturation measurement principle and the measurement results greatly influenced and heat and motion artifact. A high accuracy in order to solve these problems blood oxygen saturation measuring method has been proposed using a broadband light source that can be easily understood by the algorithm. The measurement of blood oxygen saturation based on broad-band light source has advantage of simple testing facility and easy understanding. Broadband light source based on blood oxygen saturation measuring program proposed in this paper is a combination of LabVIEW and MATLAB. Using the wavelength range of 450 nm-750 nm using a floating light absorption of oxyhemoglobin and deoxyhemoglobin to measure the blood oxygen saturation. Hand movement is to fix the probe to the motor stage in order to prevent oxygen saturation measurement that affect the sample and probe kept constant interval. Experimental results show that the proposed method noticeably increases the accuracy and saves time compared with the conventional methods.

Keywords: oxygen saturation, broad-band light source, CCD, light reflectance theory

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Authors: Changchun Liang, Xiaodong Zhang, Xin Liu, Pengfei Sun

Abstract:

Space intelligent robotic systems are expected to play an increasingly important role in the future. The robotic on-orbital service, whose key is the tracking and capturing technology, becomes research hot in recent years. In this paper, the authors propose a vision servo control system for target capturing. Robotic manipulator will be an intelligent robotic system with large-scale movement, functional agility, and autonomous ability, and it can be operated by astronauts in the space station or be controlled by the ground operator in the remote operation mode. To realize the autonomous movement and capture mission of SRM, a kind of autonomous programming strategy based on multi-camera vision fusion is designed and the selection principle of object visual position and orientation measurement information is defined for the better precision. Distributed control system hierarchy is designed and reliability is considering to guarantee the abilities of control system. At last, a ground experiment system is set up based on the concept of robotic control system. With that, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm, and demonstrates that the control system can fulfill the needs of function, real-time and reliability.

Keywords: control system, on-orbital service, space robot, vision servo

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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: Cherry Dhiman, Ayushi Paliwal, Mohd. Shahid Khan, M. N. Reddy, Vinay Gupta, Monika Tomar

Abstract:

The laser based high resolution spectroscopic experimental techniques such as Laser Induced Breakdown Spectroscopy (LIBS), Rotating Disk Electrode Optical Emission spectroscopy (RDE-OES) and Surface Plasmon Resonance (SPR) have been used for the study of composition and degradation analysis of used engine oils. Engine oils are mainly composed of aliphatic and aromatics compounds and its soot contains hazardous components in the form of fine, coarse and ultrafine particles consisting of wear metal elements. Such coarse particulates matter (PM) and toxic elements are extremely dangerous for human health that can cause respiratory and genetic disorder in humans. The combustible soot from thermal power plants, industry, aircrafts, ships and vehicles can lead to the environmental and climate destabilization. It contributes towards global pollution for land, water, air and global warming for environment. The detection of such toxicants in the form of elemental analysis is a very serious issue for the waste material management of various organic, inorganic hydrocarbons and radioactive waste elements. In view of such important points, the current study on used engine oils was performed. The fundamental characterization of engine oils was conducted by measuring water content and kinematic viscosity test that proves the crude analysis of the degradation of used engine oils samples. The microscopic quantitative and qualitative analysis was presented by RDE-OES technique which confirms the presence of elemental impurities of Pb, Al, Cu, Si, Fe, Cr, Na and Ba lines for used waste engine oil samples in few ppm. The presence of such elemental impurities was confirmed by LIBS spectral analysis at various transition levels of atomic line. The recorded transition line of Pb confirms the maximum degradation which was found in used engine oil sample no. 3 and 4. Apart from the basic tests, the calculations for dielectric constants and refractive index of the engine oils were performed via SPR analysis.

Keywords: surface plasmon resonance, laser-induced breakdown spectroscopy, ICCD spectrometer, engine oil

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Authors: Partha Sarathi Majee, S. Bhattacharyya

Abstract:

Migration of a core-shell soft particle under the influence of an external electric field in an electrolyte solution is studied numerically. The soft particle is coated with a positively charged polyelectrolyte layer (PEL) and the rigid core is having a uniform surface charge density. The Darcy-Brinkman extended Navier-Stokes equations are solved for the motion of the ionized fluid, the non-linear Nernst-Planck equations for the ion transport and the Poisson equation for the electric potential. A pressure correction based iterative algorithm is adopted for numerical computations. The effects of convection on double layer polarization (DLP) and diffusion dominated counter ions penetration are investigated for a wide range of Debye layer thickness, PEL fixed surface charge density, and permeability of the PEL. Our results show that when the Debye layer is in order of the particle size, the DLP effect is significant and produces a reduction in electrophoretic mobility. However, the double layer polarization effect is negligible for a thin Debye layer or low permeable cases. The point of zero mobility and the existence of mobility reversal depending on the electrolyte concentration are also presented.

Keywords: debye length, double layer polarization, electrophoresis, mobility reversal, soft particle

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Authors: Ruchi Pathania, Ahmad Ahmad, Shireesh Srivastava

Abstract:

Cyanobacteria is a promising microbe that can capture and convert atmospheric CO₂ and light into valuable industrial bio-products like biofuels, biodegradable plastics, etc. Among their most attractive traits are faster autotrophic growth, whole year cultivation using non-arable land, high photosynthetic activity, much greater biomass and productivity and easy for genetic manipulations. Cyanobacteria store carbon in the form of glycogen which can be hydrolyzed to release glucose and fermented to form bioethanol or other valuable products. Marine cyanobacterial species are especially attractive for countries with scarcity of freshwater. We recently identified a marine native cyanobacterium Synechococcus sp. BDU 130192 which has good growth rate and high level of polyglucans accumulation compared to Synechococcus PCC 7002. In this study, firstly we sequenced the whole genome and the sequences were annotated using the RAST server. Genome scale metabolic model (GSMM) was reconstructed through COBRA toolbox. GSMM is a computational representation of the metabolic reactions and metabolites of the target strain. GSMMs construction through the application of Flux Balance Analysis (FBA), which uses external nutrient uptake rates and estimate steady state intracellular and extracellular reaction fluxes, including maximization of cell growth. The model, which we have named isyn942, includes 942 reactions and 913 metabolites having 831 metabolic, 78 transport and 33 exchange reactions. The phylogenetic tree obtained by BLAST search revealed that the strain was a close relative of Synechococcus PCC 7002. The flux balance analysis (FBA) was applied on the model iSyn942 to predict the theoretical yields (mol product produced/mol CO₂ consumed) for native and non-native products like acetone, butanol, etc. under phototrophic condition by applying metabolic engineering strategies. The reported strain can be a viable strain for biotechnological applications, and the model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for enhanced production of various bioproducts.

Keywords: cyanobacteria, flux balance analysis, genome scale metabolic model, metabolic engineering

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