Search results for: particle union optimization algorithm

Authors: Bendjelloul Meriem, Elandaloussi El Hadj

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In this work, we studied the effectiveness of a lignocellulosic waste (wood sawdust) for the removal of cadmium Cd (II) in aqueous solution. The adsorbent material SBO-CH2-CO2Na has been prepared by alkaline pretreatment of wood sawdust followed by a chemical modification with sodium salt of chloroacetic acid. The characterization of the as-prepared material by FTIR has proven that the grafting of acetate spacer took actually place in the lignocellulosic backbone by the appearance of characteristic band of carboxylic groups in the IR spectrum. The removal study of Cd2+ by SBO-CH2-CO2Na material at the solid-liquid interface was carried out by kinetics, sorption isotherms, effect of temperature and thermodynamic parameters were evaluated. The last part of this work was dedicated to assess the regenerability of the adsorbent material after three reuse cycles. The results indicate that SBO-CH2-CO2Na matrix possesses a high effectiveness in removing Cd (II) with an adsorption capacity of 222.22 mg/g, yet a better value that those of many low-cost adsorbents so far reported in the literature. The results found in the course of this study suggest that ionic exchange is the most appropriate mechanism involved in the removal of cadmium ions.

Keywords: adsorption, cadmium, isotherms, lignocellulosic, regenerability

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Authors: Usha Sivasankaran, Seetha Raman

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Experimental work on Double skin Concrete Filled tubes (DSCFT) are a variation of CFT (Concrete- filled steel tubular) with a hollow core formed by two concentric steel tubes in – filled with concrete. Six Specimens with three different volume fractions of steel fibres are cast and tested. Experiments on circular steel tubes in – filled with steel fibre reinforced concrete (SFRC) and normal concrete have been performed to investigate the contribution of steel fibres to the load bearing capacity of Short Composite Columns. The main Variable considered in the test study is the percentage of steel fibres added to the in –filled concrete. All the specimens were tested under axial compression until failure state realisation. This project presents the percentage Variation in the compression strengths of the 3 types of Composite members taken under Study. The results show that 1.5% SFRC in filled steel columns exhibit enhanced ultimate load carrying capacity.

Keywords: composite columns, optimization of steel, double skin, DSCFT

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Authors: Raida Zouari, Iness Ahriz, Rafik Zayani, Ali Dziri, Ridha Bouallegue

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This paper proposes a room level indoor localization algorithm based on the use Multi-Layer Neural Network (MLNN) classifiers and one versus one strategy. Seven parameters of the Channel Impulse Response (CIR) were used and Gram-Shmidt Orthogonalization was performed to study the relevance of the extracted parameters. Simulation results show that when relevant CIR parameters are used as position fingerprint and when optimal MLNN architecture is selected good room level localization score can be achieved. The current study showed also that some of the CIR parameters are not correlated to the location and can decrease the localization performance of the system.

Keywords: mobile indoor localization, multi-layer neural network (MLNN), channel impulse response (CIR), Gram-Shmidt orthogonalization

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Authors: Manirath Wongsim

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Information Quality (IQ) has become a critical, strategic issue in Accounting Information Systems (AIS) adoption. In order to implement AIS adoption successfully, it is important to consider the quality of information use throughout the adoption process, which seriously impacts the effectiveness of AIS adoption practice and the optimization of AIS adoption decisions. There is a growing need for research to provide insights into issues and solutions related to IQ in AIS adoption. The need for an integrated approach to improve IQ in AIS adoption, as well as the unique characteristics of accounting data, demands an AIS adoption specific IQ framework. This research aims to explore ways of managing information quality and AIS adoption to investigate the relationship between the IQ issues and AIS adoption process. This study has led to the development of a framework for understanding IQ management in AIS adoption. This research was done on 44 respondents as ten organisations from manufacturing firms in Thailand. The findings of the research’s empirical evidence suggest that IQ dimensions in AIS adoption to provide assistance in all process of decision making. This research provides empirical evidence that information quality of AIS adoption affect decision making and suggests that these variables should be considered in adopting AIS in order to improve the effectiveness of AIS.

Keywords: information quality, information quality dimensions, accounting information systems, accounting information system adoption

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Authors: Nadia Akkari

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This paper presents the analysis and simulation of the control of double star induction motor, using direct torque control (DTC) based on RST regulator. The DTC is an excellent solution for general- purpose induction drives in very wide range the short sampling time required by the TC schemes makes them suited to a very fast torque and flux controlled drives as well the simplicity of the control algorithm. DTC is inherently a motion sensorless control method. The RST regulator can improve the double star induction motor performance in terms of overshoot, rapidity, cancellation of disturbance, and capacity to maintain a high level of performance. Simulation results indicate that the proposed regulator has better performance responses. The implementation of the DTC applied to a double star induction motor based on RST regulator is validated with simulated results.

Keywords: Direct Torque Control (DTC), Double Star Induction Motor (DSIM), RST Regulator

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Qing K. Zhu

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Very Large Scale Integration (VLSI) design becomes very complex due to the continuous integration of millions of gates in one chip based on Moore’s law. Designers have encountered numerous report files during design iterations using timing and noise analysis tools. This paper presented our work using data mining techniques combined with HTML tables to extract and represent critical timing/noise data. When we apply this data-mining tool in real applications, the running speed is important. The software employs table look-up techniques in the programming for the reasonable running speed based on performance testing results. We added several advanced features for the application in one industry chip design.

Keywords: VLSI design, data mining, big data, HTML forms, web, VLSI, EDA, timing, noise

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Authors: Bhairi Lakshminarayana, Surajit Sarker, Ch. Subrahmanyam

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The present study reports the development of noble metal free nano catalysts for low-temperature CO oxidation and water gas shift reaction. Mn-substituted CeO2 solid solution catalysts were synthesized by co-precipitation, combustion and hydrothermal methods. The formation of solid solution was confirmed by XRD with Rietveld refinement and the percentage of carbon and nitrogen doping was ensured by CHNS analyzer. Raman spectroscopic confirmed the oxygen vacancies. The surface area, pore volume and pore size distribution confirmed by N2 physisorption analysis, whereas, UV-visible diffuse reflectance spectroscopy and XPS data confirmed the oxidation state of the Mn ion. The particle size and morphology (spherical shape) of the material was confirmed using FESEM and HRTEM analysis. Ce0.8Mn0.2O2-δ was calcined at 400 °C, 600 °C and 800 °C. Raman spectroscopy confirmed that the catalyst calcined at 400 °C has the best redox properties. The activity of the designed catalysts for CO oxidation (0.2 vol%), carried out with GHSV of 21,000 h-1 and it has been observed that co-precipitation favored the best active catalyst towards CO oxidation and water gas shift reaction, due to the high surface area, improved reducibility, oxygen mobility and highest quantity of surface oxygen species. The activation energy of low temperature CO oxidation on Ce0.8Mn0.2O2- δ (combustion) was 5.5 kcal.K-1.mole-1. The designed catalysts were tested for water gas shift reaction. The present study demonstrates that Mn ion substituted ceria at 400 °C calcination temperature prepared by co-precipitation method promise to revive a green sustainable energy production approach.

Keywords: Ce0.8Mn0.2O2-ð, CO oxidation, physicochemical characterization, water gas shift reaction (WGS)

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Andrey Yatsun, Andrei Malchikov

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Exoskeletons are used to support and compensate for the load on the human musculoskeletal system. Elastomers are an important component of exoskeletons, providing additional support and compensating for the load. The algorithm of the active elastomer tension system provides the required auxiliary force depending on the angle of rotation and the tilt speed of the operator's torso. Feedback for the drive is provided by a force sensor integrated into the attachment of the exoskeleton vest. The use of direct force measurement ensures the required accuracy in all settings of the man-machine system. Non-adjustable elastic elements make it difficult to move without load, tilt forward and walk. A strategy for the organization of the auxiliary forces management system is proposed based on the allocation of 4 operating modes of the human-machine system.

Keywords: soft exoskeleton, mathematical modeling, pre-tension elastomer, human-machine interaction

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Authors: Mohamed E. Hassan

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Through numerous chemical linkages, historical textiles in burial contexts or in museums are exposed to many different forms of stains and filth. The cleaning procedure must be carried out carefully without causing any irreparable harm, and sediments must be removed without damaging the surface's original material. Science and technology continue to develop novel methods for cleaning historical textiles and artistic surfaces biologically (using enzymes). Lipase and α-amylase were immobilized on nanoparticles of alginate/tea waste nanoparticle complex and used in historical textile cleaning. The preparation of nanoparticles, activation, and enzyme immobilization were characterized. Optimization of loading times and units of the two enzymes was done. It was found that the optimum time and units of amylase were 3 hours and 30 U, respectively. While the optimum time and units of lipase were 2.5 hours and 20 U, respectively, FT-IR and TGA instruments were used in proving the preparation of nanoparticles and the immobilization process. SEM was used to examine the fibres before and after treatment. In conclusion, a new carrier was prepared from alginate/Tea waste and optimized to be used in the restoration and conservation of historical textiles using immobilized lipase and α-amylase.

Keywords: alginate/tea waste, nanoparticles, immobilized enzymes, historical textiles

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Authors: Sumachaya Harnsukworapanich, Tetsuo Ichimori

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The apportionment method is used by many countries, to calculate the distribution of seats in political bodies. For example, this method is used in the United States (U.S.) to distribute house seats proportionally based on the population of the electoral district. Famous apportionment methods include the divisor methods called the Adams Method, Dean Method, Hill Method, Jefferson Method and Webster Method. Sometimes the results from the implementation of these divisor methods are unfair and include errors. Therefore, it is important to examine the optimization of this method by using a bias measurement to figure out precise and fair results. In this research we investigate the bias of divisor methods in the U.S. Houses of Representatives toward large and small states by applying the Stolarsky Mean Method. We compare the bias of the apportionment method by using two famous bias measurements: The Balinski and Young measurement and the Ernst measurement. Both measurements have a formula for large and small states. The Third measurement however, which was created by the researchers, did not factor in the element of large and small states into the formula. All three measurements are compared and the results show that our measurement produces similar results to the other two famous measurements.

Keywords: apportionment, bias, divisor, fair, measurement

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Authors: Ling Xu, Giuseppe Loprencipe, Antonio D'Andrea

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Asphalt pavement with recycled and sustainable materials has become the most commonly adopted strategy for road construction, including reclaimed asphalt pavement (RAP) and crumb rubber (CR) from waste tires. However, the adhesion and cohesion characteristics of rubberized reclaimed asphalt pavement were still ambiguous, resulting in deteriorated adhesion behavior and life performance. This research investigated the effect of bonding characteristics on rutting resistance and moisture susceptibility of rubberized reclaimed asphalt pavement in terms of two RAP sources with different oxidation levels and two tire rubber with different particle sizes. Firstly, the binder bond strength (BBS) test and bonding failure distinguishment were conducted to analyze the surface behaviors of binder-aggregate interaction. Then, the compatibility and penetration grade of rubberized RAP binder were evaluated by rotational viscosity test and penetration test, respectively. Hamburg wheel track (HWT) test with high-temperature viscoelastic deformation analysis was adopted, which illustrated the rutting resistance. Additionally, a water boiling test was employed to evaluate the moisture susceptibility of the mixture and the texture features were characterized with the statistical parameters of image colors. Finally, the colloid structure model of rubberized RAP binder with surface interaction was proposed, and statistical analysis was established to release the correlation among various indexes. This study concluded that the gel-phase colloid structure and molecular diffusion of the free light fraction would affect the surface interpretation with aggregate, determining the bonding characteristic of rubberized RAP asphalt.

Keywords: bonding characteristics, reclaimed asphalt pavement, rubberized asphalt, sustainable material

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Authors: Muhammad Rizwan Tabassum, Ao Xia, Jerry D. Murphy

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The aim of this work is to provide an overview of macroalgae as an alternative feedstock for gaseous fuel production and key innovative technologies. Climate change and continuously depleting resources are the key driving forces to think for alternative sources of energy. Macroalgae can be favored over land based energy crops because they are not in direct competition with food crops. However, some drawbacks, such as high moisture content, seasonal variation in chemical composition and process inhibition limit the economic practicability. Macroalgae, like brown seaweed can be converted into gaseous and liquid fuel by different conversion technologies. Biomethane via anaerobic digestion is the appealing technology due to its dual advantage of a commercially applicable and environment friendly technology. Other technologies like biodiesel and bioethanol conversion technologies from seaweed are still under progress. Screening of high yielding macroalgae species, peak harvesting season and process optimization make the technology economically feasible for alternative source of feedstock for biofuel production in future.

Keywords: anaerobic digestion, biofuels, bio-methane, advanced conversion technologies, macroalgae

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Authors: Suheyla Kocaman, Gulnare Ahmetli, Alaaddin Cerit, Alize Yucel, Merve Gozukucuk

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Shell wastes represent a considerable quantity of byproducts in the shellfish aquaculture. From the viewpoint of ecofriendly and economical disposal, it is highly desirable to convert these residues into high value-added products for industrial applications. So far, the utilization of shell wastes was confined at relatively lower levels, e.g. wastewater decontaminant, soil conditioner, fertilizer constituent, feed additive and liming agent. Shell wastes consist of calcium carbonate and organic matrices, with the former accounting for 95-99% by weight. Being the richest source of biogenic CaCO₃, shell wastes are suitable to prepare high purity CaCO₃ powders, which have been extensively applied in various industrial products, such as paper, rubber, paints and pharmaceuticals. Furthermore, the shell waste could be further processed to be the filler of polymer composites. This paper presents a study on the potential use of mussel shell waste as biofiller to produce the composite materials with different epoxy matrices, such as bisphenol-A type, CTBN modified and polyurethane modified epoxy resins. Morphology and mechanical properties of shell particles reinforced epoxy composites were evaluated to assess the possibility of using it as a new material. The effects of shell particle content on the mechanical properties of the composites were investigated. It was shown that in all composites, the tensile strength and Young’s modulus values increase with the increase of mussel shell particles content from 10 wt% to 50 wt%, while the elongation at break decreased, compared to pure epoxy resin. The highest Young’s modulus values were determined for bisphenol-A type epoxy composites.

Keywords: biocomposite, epoxy resin, mussel shell, mechanical properties

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Authors: Shivani Singh, Shantanu Nakhare, Kalyani Nair, Rohan Shetty

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As social networking is growing at a rapid pace today it is vital that we work on improving its management. Research has shown that the content present in online social networks may have significant influence on impressionable minds. If such platforms are misused, it will lead to negative consequences. Detecting insults or inappropriate messages continues to be one of the most challenging aspects of Online Social Networks (OSNs) today. We address this problem through a Machine Learning Based Soft Text Classifier approach using Support Vector Machine algorithm. The proposed system acts as a screening mechanism the alerts the user about such messages. The messages are classified according to their subject matter and each comment is labeled for the presence of profanity and insults.

Keywords: machine learning, online social networks, soft text classifier, support vector machine

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

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Authors: Kartik Gupta, Umar Khan, Mayur Parab, Dhiraj Chaudhari, Afzal Ansari

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The report focuses on the study related to the Design and Simulation of a stabilator (an all-movable horizontal stabilizer) for a fixed-wing aircraft. The project involves the development of a computerized direct optimization procedure for designing an aircraft all-movable stabilator. This procedure evaluates various design variables to synthesize an optimal stabilator that meets specific requirements, including performance, control, stability, strength, and flutter velocity constraints. The work signifies the CFD (Computational Fluid Dynamics) analysis of the airfoils used in the stabilator along with the CFD analysis of the Stabilizer and Stabilator of an aircraft named Thorp- T18 in software like XFLR5 and ANSYS-Fluent. A comparative analysis between a Stabilizer and Stabilator of equal surface area and under the same environmental conditions was done, and the percentage of drag reduced by the Stabilator for the same amount of lift generated as the Stabilizer was also calculated lastly, Wind tunnel testing was performed on a scale down model of the Stabilizer and Stabilator and the results of the Wind tunnel testing were compared with the results of CFD.

Keywords: wind tunnel testing, CFD, stabilizer, stabilator

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Authors: Bienvenu Gael Fouda Mbanga

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This study reports a new approach of preparation of carbon nanoparticles coated cerium oxide nanorods (CNPs/CeONRs) nanocomposite and reusing the spent adsorbent of Cd2+- CNPs/CeONRs nanocomposite for latent fingerprint detection (LFP) after removing Cd2+ ions from aqueous solution. CNPs/CeONRs nanocomposite was prepared by using CNPs and CeONRs with adsorption processes. The prepared nanocomposite was then characterized by using UV-visible spectroscopy (UV-visible), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction pattern (XRD), scanning electron microscope (SEM), Transmission electron microscopy (TEM), Energy-dispersive X-ray spectroscopy (EDS), Zeta potential, X-ray photoelectron spectroscopy (XPS). The average size of the CNPs was 7.84nm. The synthesized CNPs/CeONRs nanocomposite has proven to be a good adsorbent for Cd2+ removal from water with optimum pH 8, dosage 0. 5 g / L. The results were best described by the Langmuir model, which indicated a linear fit (R2 = 0.8539-0.9969). The adsorption capacity of CNPs/CeONRs nanocomposite showed the best removal of Cd2+ ions with qm = (32.28-59.92 mg/g), when compared to previous reports. This adsorption followed pseudo-second order kinetics and intra particle diffusion processes. ∆G and ∆H values indicated spontaneity at high temperature (40oC) and the endothermic nature of the adsorption process. CNPs/CeONRs nanocomposite therefore showed potential as an effective adsorbent. Furthermore, the metal loaded on the adsorbent Cd2+- CNPs/CeONRs has proven to be sensitive and selective for LFP detection on various porous substrates. Hence Cd2+-CNPs/CeONRs nanocomposite can be reused as a good fingerprint labelling agent in LFP detection so as to avoid secondary environmental pollution by disposal of the spent adsorbent.

Keywords: Cd2+-CNPs/CeONRs nanocomposite, cadmium adsorption, isotherm, kinetics, thermodynamics, reusable for latent fingerprint detection

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Authors: Edgar Willy Rimarachin Valderrama, Eduardo Rojas Parra

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Thermal energy from solar radiation is widely present in power plants, food drying, chemical reactors, heating and cooling systems, water treatment processes, hydrogen production, and others. In the case of power plants, one of the technologies available to transform solar energy into thermal energy is by solar rotary kilns where a bed of granular matter is heated through concentrated radiation obtained from an arrangement of heliostats. Numerical modeling is a useful approach to study the behavior of granular beds in solar rotary kilns. This technique, once validated with small-scale experiments, can be used to simulate large-scale processes for industrial applications. This study gives a comprehensive classification of numerical models used to simulate the movement and heat transfer for beds of granular media within solar rotary furnaces. In general, there exist three categories of models: 1) continuum, 2) discrete, and 3) multiphysics modeling. The continuum modeling considers zero-dimensional, one-dimensional and fluid-like models. On the other hand, the discrete element models compute the movement of each particle of the bed individually. In this kind of modeling, the heat transfer acts during contacts, which can occur by solid-solid and solid-gas-solid conduction. Finally, the multiphysics approach considers discrete elements to simulate grains and a continuous modeling to simulate the fluid around particles. This classification allows to compare the advantages and disadvantages for each kind of model in terms of accuracy, computational cost and implementation.

Keywords: granular beds, numerical models, rotary kilns, solar thermal applications

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Authors: Steven Walker

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This paper assesses recent data on the prevalence of poverty among children and young people diagnosed with mental health problems. The paper will demonstrate that the current hierarchy of risk factors for developing mental health problems needs adjusting to place poverty among the highest risk factors. Globally poverty is calculated to keep rising especially among less developed countries, and the post-Covid 19 economic recession in developed countries is set to rise. The experience of young people enduring Pandemic isolation is already being quantified and is expected to increase referrals for specialist intervention. Searches on several medical/psychological/social databases using keywords: poverty, children, mental illness were undertaken between 2018 and 2021. Worldwide, 700 million people still live in extreme poverty, half of whom are children. Children are physically and mentally disproportionately affected. Children who grow up impoverished lack the basic necessities they need to survive and thrive. 150 million children have been plunged into multidimensional poverty due to COVID-19. The poorest children are twice as likely to die in childhood than their wealthier peers. For those growing up in humanitarian crises such as Ukraine, the risks of deprivation and exclusion are magnified. In the world’s richest countries, one in seven children still live in poverty. Currently, one in four children in the European Union are at risk of falling into poverty. In Europe the impact of Brexit on the UK economy is predicted to reduce GDP by 5% in 2021 with a corresponding rise in poverty. According to the global charity Oxfam wealth inequality impacts levels of child abuse and affects women and girls worse and is a contributory factor in the risk of developing childhood mental illness. In the UK 2000 Foodbanks have opened since 2010, handing out 2 million food parcels annually, where there are currently 4 million children officially living in poverty. This research demonstrates that there is a strong association between families’ socio-economic circumstances and the chances that their children will experience mental illness. Evidence of this association is found repeatedly across developed countries. The paper will conclude by arguing that psychologists, psychiatrists, psychotherapists, social workers and CAMHS specialists need to place more importance on this critical socio-economic variable when assessing referred children and also advocate for political priorities in governments to reduce poverty and lower the risk of childhood mental illness.

Keywords: poverty, resilience, risk factor, socio economic, susceptibility

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Mahdi Zolfaghari, Seyed Hossein Hosseinian, Hossein Askarian Abyaneh, Mehrdad Abedi

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This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability.

Keywords: electric vehicle, model predictive control, power quality, V2G technology, virtual active power filter

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Authors: Nigel Q. Kelly, Zaid Siddiqi, Jin W. Lee

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It is known that using Coanda aerosurfaces can drastically augment the lift forces when applied to an Unmanned Aerial Vehicle (UAV) platform. However, Coanda saucer UAVs, which commonly use a dish-like, radially-extending structure, have shown no significant increases in thrust/lift force and therefore have never been commercially successful: the additional thrust/lift generated by the Coanda surface diminishes since the airstreams emerging from the rotor compartment expand radially causing serious loss of momentums and therefore a net loss of total thrust/lift. To overcome this technical weakness, we propose to examine a Coanda surface of straight, cylindrical design and optimize its geometry for highest thrust/lift utilizing computational fluid dynamics software ANSYS Fluent®. The results of this study reveal that a Coanda UAV configured with 4 sides of straight, cylindrical Coanda surface achieve an overall 45% increase in lift compared to conventional Coanda Saucer UAV configurations. This venture integrates with an ongoing research project where a Coanda prototype is being assembled. Additionally, a custom thrust-stand has been constructed for thrust/lift measurement.

Keywords: CFD, Coanda, lift, UAV

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Authors: Sajidullah Khan, Najeeb Ullah, Wang Yin Chai, Chai Soo See

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In this paper, we propose an approach to remove impulse and Poisson noise from X-ray images. Many filters have been used for impulse noise removal from color and gray scale images with their own strengths and weaknesses but X-ray images contain Poisson noise and unfortunately there is no intelligent filter which can detect impulse and Poisson noise from X-ray images. Our proposed filter uses the upgraded layer discrimination approach to detect both Impulse and Poisson noise corrupted pixels in X-ray images and then restores only those detected pixels with a simple efficient and reliable one line equation. Our Proposed algorithms are very effective and much more efficient than all existing filters used only for Impulse noise removal. The proposed method uses a new powerful and efficient noise detection method to determine whether the pixel under observation is corrupted or noise free. Results from computer simulations are used to demonstrate pleasing performance of our proposed method.

Keywords: X-ray image de-noising, impulse noise, poisson noise, PRWF

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Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Ahteshamul Haq, Srikant Gupta, Murshid Kamal, Irfan Ali

Abstract:

This case study designs and builds a multi-objective production planning model for a hardware firm with certain & uncertain data. During the time of interaction with the manager of the firm, they indicate some of the parameters may be vague. This vagueness in the formulated model is handled by the concept of fuzzy set theory. Triangular & Trapezoidal fuzzy numbers are used to represent the uncertainty in the collected data. The fuzzy nature is de-fuzzified into the crisp form using well-known defuzzification method via graded mean integration representation method. The proposed model attempts to maximize the production of the firm, profit related to the manufactured items & minimize the carrying inventory costs in both certain & uncertain environment. The recommended optimal plan is determined via fuzzy programming approach, and the formulated models are solved by using optimizing software LINGO 16.0 for getting the optimal production plan. The proposed model yields an efficient compromise solution with the overall satisfaction of decision maker.

Keywords: production planning problem, multi-objective optimization, fuzzy programming, fuzzy sets

Procedia PDF Downloads 211

Authors: Xiaoci Li, Yonghua Huang, Hui Lin

Abstract:

Optimization of the concentration of components in mixed refrigerants leads to potential improvement of either thermodynamic cycle performance or safety performance of heat pumps and refrigerators. R32+R1234yf and R125+R290 are two promising binary mixed refrigerants for the application of heat pumps working in the cold areas. The p-ρ-T data of these mixtures are one of the fundamental and necessary properties for design and evaluation of the performance of the heat pumps. Although the property data of mixtures can be predicted by the mixing models based on the pure substances incorporated in programs such as the NIST database Refprop, direct property measurement will still be helpful to reveal the true state behaviors and verify the models. Densities of the mixtures of R32+R1234yf an d R125+R290 are measured by an Anton Paar U shape oscillating tube digital densimeter DMA-4500 in the range of temperatures from 0°C to 100 °C and pressures up to 10 MPa. The accuracy of the measurement reaches 0.00005 g/cm³. The experimental data are compared with the predictions by Refprop in the corresponding range of pressure and temperature.

Keywords: mixed refrigerant, density measurement, densimeter, thermodynamic property

Procedia PDF Downloads 294

Authors: Tugrul Oktay, Mehmet Konar, Mohamed Abdallah Mohamed, Murat Aydin, Firat Sal, Murat Onay, Mustafa Soylak

Abstract:

In this paper, it is aimed to improve autonomous flight performance of a load-carrying (payload: 3 kg and total: 6kg) unmanned aerial vehicle (UAV) through active wing and horizontal tail active morphing and also integrated autopilot system parameters (i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing and horizontal tail during flight) design. For this purpose, a loadcarrying UAV (i.e. ZANKA-II) is manufactured in Erciyes University, College of Aviation, Model Aircraft Laboratory is benefited. Optimum values of UAV parameters and autopilot parameters are obtained using a stochastic optimization method. Using this approach autonomous flight performance of UAV is substantially improved and also in some adverse weather conditions an opportunity for safe flight is satisfied. Active morphing and integrated design approach gives confidence, high performance and easy-utility request of UAV users.

Keywords: unmanned aerial vehicles, morphing, autopilots, autonomous performance

Procedia PDF Downloads 671