Search results for: optimization of steel

Authors: M. Dabirinezhad, M. Bayat Pour, A. Dabirinejad

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In 2020, the technology was introduced to solve some of the deficiencies of direct digital radiology. SDDR is an invention that is capable of capturing dental images without human intervention, and it was invented by the authors of this paper. Adjusting the radiology wave dose is a part of the dentists, radiologists, and dental nurses’ tasks during the radiology photography process. In this paper, an improvement will be added to enable SDDR to set the suitable radiology wave dose according to the density and age of the patients automatically. The separate sensors will be included in the sensors’ package to use the ultrasonic wave to detect the density of the teeth and change the wave dose. It facilitates the process of dental photography in terms of time and enhances the accuracy of choosing the correct wave dose for each patient separately. Since the radiology waves are well known to trigger off other diseases such as cancer, choosing the most suitable wave dose can be helpful to decrease the side effect of that for human health. In other words, it decreases the exposure time for the patients. On the other hand, due to saving time, less energy will be consumed, and saving energy can be beneficial to decrease the environmental impact as well.

Keywords: dental direct digital imaging, environmental impacts, SDDR technology, wave dose

Procedia PDF Downloads 194

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 111

Authors: Juan Alfredo Guevara Carrio, Swamy Toolahalli Thipperudra, Riddhi Naik Dharmeshbhai, Sergio Graniero Echeverrigaray, Jose Vitorio Emiliano, Antonio Helio Castro

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In order to advance the hydrogen economy, several industrial sectors can potentially benefit from the trillions of stimulus spending for post-coronavirus. Blending hydrogen into natural gas pipeline networks has been proposed as a means of delivering it during the early market development phase, using separation and purification technologies downstream to extract the pure H₂ close to the point of end-use. This first step has been mentioned around the world as an opportunity to use existing infrastructures for immediate decarbonisation pathways. Among current technologies used to extract hydrogen from mixtures in pipelines or liquid carriers, membrane separation can achieve the highest selectivity. The most efficient approach for the separation of H₂ from other substances by membranes is offered from the research of 2D layered materials due to their exceptional physical and chemical properties. Graphene-based membranes, with their distribution of pore sizes in nanometers and angstrom range, have shown fundamental and economic advantages over other materials. Their combination with the structure of ceramic and geopolymeric materials enabled the synthesis of nanocomposites and the fabrication of membranes with long-term stability and robustness in a relevant range of physical and chemical conditions. Versatile separation modules have been developed for hydrogen separation, which adaptability allows their integration in industrial prototypes for applications in heavy transport, steel, and cement production, as well as small installations at end-user stations of pipeline networks. The developed membranes and prototypes are a practical contribution to the technological challenge of supply pure H₂ for the mentioned industries as well as hydrogen energy-based fuel cells.

Keywords: graphene nano-composite membranes, hydrogen separation and purification, separation modules, indsutrial prototype

Procedia PDF Downloads 159

Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

Procedia PDF Downloads 282

Authors: Mukesh Kumar, Mahendra Pal Sharma

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Depleting fossil fuel resources coupled with serious environmental degradation has led to the search for alternative resources for biodiesel production as a substitute of Petro-diesel. Currently, edible, non-edible oils and microalgal plant species are cultivated for biodiesel production. Looking at the demerits of edible and non-edible oil resources, the focus is being given to grow microalgal species having high oil productivities, less maturity time and less land requirement. Out of various microalgal species, Chlorella protothecoides is considered as the most promising species for biodiesel production owing to high oil content (58 %), faster growth rate (24–48 h) and high biomass productivity (1214 mg/l/day). The present paper reports the results of optimization of reaction parameters of transesterification process as well as the kinetics of transesterification with 97% yield of biodiesel. The measurement of fuel quality of microalgal biodiesel shows that the biodiesel exhibit very good oxidation stability (O.S) of 7 hrs, more than ASTM D6751 (3 hrs) and EN 14112 (6 hrs) specifications. The CP and PP of 0 and -3 °C are finding as per ASTM D 2500-11 and ASTM D 97-12 standards. These results show that the microalgal biodiesel does not need any enhancement in O.S & CFP and hence can be recommended to be directly used as MB100 or its blends into diesel engine operation. Further, scope is available for the production of binary blends using poor quality biodiesel for engine operation.

Keywords: fuel quality, methyl ester yield, microalgae, transesterification

Procedia PDF Downloads 215

Authors: Kwame B. O. Amoah

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This paper focuses on retrofitting an old existing office building to a net-zero energy building (NZEB). An existing small office building in Melbourne, Florida, was chosen as a case study to integrate state-of-the-art design strategies and energy-efficient building systems to improve building performance and reduce energy consumption. The study aimed to explore possible ways to maximize energy savings and renewable energy generation sources to cover the building's remaining energy needs necessary to achieve net-zero energy goals. A series of retrofit options were reviewed and adopted with some significant additional decision considerations. Detailed processes and considerations leading to zero energy are well documented in this study, with lessons learned adequately outlined. Based on building energy simulations, multiple design considerations were investigated, such as emerging state-of-the-art technologies, material selection, improvements to the building envelope, optimization of the HVAC, lighting systems, and occupancy loads analysis, as well as the application of renewable energy sources. The comparative analysis of simulation results was used to determine how specific techniques led to energy saving and cost reductions. The research results indicate this small office building can meet net-zero energy use after appropriate design manipulations and renewable energy sources.

Keywords: energy consumption, building energy analysis, energy retrofits, energy-efficiency

Procedia PDF Downloads 223

Authors: Hee-Jae Suh, Kyung-Su Kim, Min-Ji Kim, Yeon-Seong Jeong, Ok-Hwan Lee, Jae-Wook Shin, Hyang-Sook Chun, Chan Lee

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Canthaxanthin is keto-carotenoid produced from beta-carotene and it has been approved to be used in many countries as a food coloring agent. Canthaxanthin has been analyzed using High Performance Liquid Chromatography (HPLC) system with various ways of pretreatment methods. Four official methods for verification of canthaxanthin at FSA (UK), AOAC (US), EFSA (EU) and MHLW (Japan) were compared to improve its analytical and the pretreatment method. The Linearity, the limit of detection (LOD), the limit of quantification (LOQ), the accuracy, the precision and the recovery ratio were determined from each method with modification in pretreatment method. All HPLC methods exhibited correlation coefficients of calibration curves for canthaxanthin as 0.9999. The analysis methods from FSA, AOAC, and MLHW showed the LOD of 0.395 ppm, 0.105 ppm, and 0.084 ppm, and the LOQ of 1.196 ppm, 0.318 ppm, 0.254 ppm, respectively. Among tested methods, HPLC method of MHLW with modification in pretreatments was finally selected for the analysis of canthaxanthin in lab, because it exhibited the resolution factor of 4.0 and the selectivity of 1.30. This analysis method showed a correlation coefficients value of 0.9999 and the lowest LOD and LOQ. Furthermore, the precision ratio was lower than 1 and the accuracy was almost 100%. The method presented the recovery ratio of 90-110% with modification in pretreatment method. The cross-validation of coefficient variation was 5 or less among tested three institutions in Korea.

Keywords: analytic method, canthaxanthin, food colorants, pretreatment method

Procedia PDF Downloads 683

Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

Procedia PDF Downloads 68

Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

Procedia PDF Downloads 140

Authors: Ghasem Sharifi, Hamed Shahmohamadi Ousaloo, Milad Azimi, Mehran Mirshams

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The ever-increasing use of satellites demands a search for increasingly accurate and reliable pointing systems. Reaction wheels are rotating devices used commonly for the attitude control of the spacecraft since provide a wide range of torque magnitude and high reliability. The numerical modeling of this device can significantly enhance the accuracy of the satellite control in space. Modeling the wheel rotation in the presence of the various frictions is one of the critical parts of this approach. This paper presents a Dynamic Model Control of a Reaction Wheel (DMCR) in the current control mode. In current-mode, the required current is delivered to the coils in order to achieve the desired torque. During this research, all the friction parameters as viscous and coulomb, motor coefficient, resistance and voltage constant are identified. In order to model identification of a reaction wheel, numerous varying current commands apply on the particular wheel to verify the estimated model. All the parameters of DMCR are identified by classical Levenberg-Marquardt (CLM) optimization method. The experimental results demonstrate that the developed model has an appropriate precise and can be used in the satellite control simulation.

Keywords: experimental modeling, friction parameters, model identification, reaction wheel

Procedia PDF Downloads 233

Authors: Mohammed Elsayed Lotfy Elsayed Abouzeid, Tomonobu Senjyu

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A novel multi-objective park and ride control approach is presented in this research. Park and ride will encourage the owners of the vehicles to leave their cars in the nearest points (on the edges of the crowded cities) and use public transportation facilities (train, bus, metro, or mon-rail) to reach their work inside the crowded city. The proposed control scheme is used to design electric vehicle charging stations (EVCS) to charge 1000 electric vehicles (EV) during their owners' work time. Cairo, Egypt is used as a case study. Photovoltaic (PV) and battery energy storage system (BESS) are used to meet the EVCS demand. Two multi-objective optimization techniques (MOGA and epsilon-MOGA) are utilized to get the optimal sizes of PV and BESS so as to meet the load demand and minimize the total life cycle cost. Detailed analysis and comparison are held to investigate the performance of the proposed control scheme using MATLAB.

Keywords: Battery Energy Storage System, Electric Vehicle, Park and Ride, Photovoltaic, Multi-objective

Procedia PDF Downloads 144

Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

Procedia PDF Downloads 354

Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan

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Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.

Keywords: CVRPTW, optimal route, depot, tabu search algorithm

Procedia PDF Downloads 136

Authors: Gamze Karanfil Celep, Kevser Dincer

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The effects of polymer concentration and electrospinning process parameters on the average diameters of electrospun polyacrylonitrile (PAN) nanofibers were experimentally investigated. Besides, mechanical and thermal properties of PAN nanofibers were examined by tensile test and thermogravimetric analysis (TGA), respectively. For this purpose, the polymer concentration, solution feed rate, supply voltage and tip-to-collector distance were determined as the control factors. To succeed these aims, Taguchi’s L16 orthogonal design (4 parameters, 4 level) was employed for the experimental design. Optimal electrospinning conditions were defined using the signal-to-noise (S/N) ratio that was calculated from diameters of the electrospun PAN nanofibers according to "the-smaller-the-better" approachment. In addition, analysis of variance (ANOVA) was evaluated to conclude the statistical significance of the process parameters. The smallest diameter of PAN nanofibers was observed. According to the S/N ratio response results, the most effective parameter on finding out of nanofiber diameter was determined. Finally, the Taguchi design of experiments method has been found to be an effective method to statistically optimize the critical electrospinning parameters used in nanofiber production. After determining the optimum process parameters of nanofiber production, electrical conductivity and fuel cell performance of electrospun PAN nanofibers on the carbon papers will be evaluated.

Keywords: nanofiber, electrospinning, polyacrylonitrile, Taguchi method

Procedia PDF Downloads 206

Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

Procedia PDF Downloads 196

Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

Procedia PDF Downloads 302

Authors: Mohammadreza Heydariazad

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Productivity growth of wind energy as a clean source needed to achieve improved strategy in production and transmission and management of wind resources in order to increase quality of power and reduce costs. New technologies based on power converters that cause changing turbine speed to suit the wind speed blowing turbine improve extraction efficiency power from wind. This article introduces variable speed wind turbines and optimization of power, and presented methods to use superconducting inductor in the composition of power converter and is proposed the dc measurement for the wind farm and especially is considered techniques available to them. In fact, this article reviews mechanisms and function, changes of wind speed turbine according to speed control strategies of various types of wind turbines and examines power possible transmission and ac from producing location to suitable location for a strong connection integrating wind farm generators, without additional cost or equipment. It also covers main objectives of the dynamic control of wind turbines, and the methods of exploitation and the ways of using it that includes the unique process of these components. Effective algorithm is presented for power control in order to extract maximum active power and maintains power factor at the desired value.

Keywords: wind energy, generator, superconducting inductor, wind turbine power

Procedia PDF Downloads 327

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

Procedia PDF Downloads 126

Authors: Aarthi Sridhar, Henri Gavin, Karah Kelly

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Rolling Isolation Systems (RISs) are simple and effective means to mitigate earthquake hazards to equipment in critical and precious facilities, such as hospitals, network collocation facilities, supercomputer centers, and museums. The RIS works by isolating components acceleration the inertial forces felt by the subsystem. The RIS consists of two platforms with counter-facing concave surfaces (dishes) in each corner. Steel balls lie inside the dishes and allow the relative motion between the top and bottom platform. Formerly, a mathematical model for the dynamics of RISs was developed using Lagrange’s equations (LE) and experimentally validated. A new mathematical model was developed using Gauss’s Principle of Least Constraint (GPLC) and verified by comparing impulse response trajectories of the GPLC model and the LE model in terms of the peak displacements and accelerations of the top platform. Mathematical models for the RIS are tedious to derive because of the non-holonomic rolling constraints imposed on the system. However, using Gauss’s Principle of Least constraint to find the equations of motion removes some of the obscurity and yields a system that can be easily extended. Though the GPLC model requires more state variables, the equations of motion are far simpler. The non-holonomic constraint is enforced in terms of accelerations and therefore requires additional constraint stabilization methods in order to avoid the possibility that numerical integration methods can cause the system to go unstable. The GPLC model allows the incorporation of more physical aspects related to the RIS, such as contribution of the vertical velocity of the platform to the kinetic energy and the mass of the balls. This mathematical model for the RIS is a tool to predict the motion of the isolation platform. The ability to statistically quantify the expected responses of the RIS is critical in the implementation of earthquake hazard mitigation.

Keywords: earthquake hazard mitigation, earthquake isolation, Gauss’s Principle of Least Constraint, nonlinear dynamics, rolling isolation system

Procedia PDF Downloads 250

Authors: Halison da Silva Pereira, Ciro Sobrinho Campolina Martins, Vitor Mainenti Leal Lopes

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Long range performance is a goal for aircraft configuration optimization. Blended Wing Body (BWB) is presented in many works of literature as the most aerodynamically efficient design for a fixed-wing aircraft. Because of its high weight to thrust ratio, BWB is the ideal configuration for many Unmanned Aerial Vehicle (UAV) missions on geomatics applications. In this work, a BWB aerodynamic design for typical light geomatics payload is presented. Aerodynamic non-dimensional coefficients are predicted using low Reynolds number computational techniques (3D Panel Method) and wing parameters like aspect ratio, taper ratio, wing twist and sweep are optimized for high cruise performance and flight quality. The methodology of this work is a summary of tailless aircraft wing design and its application, with appropriate computational schemes, to light UAV subjected to low Reynolds number flows leads to conclusions like the higher performance and flight quality of thicker airfoils in the airframe body and the benefits of using aerodynamic twist rather than just geometric.

Keywords: blended wing body, low Reynolds number, panel method, UAV

Procedia PDF Downloads 586

Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

Procedia PDF Downloads 441

Authors: Shoumodip Roy, Ankit Singhania, K. R. K. Rao, Ravi Shankar, M. K. Agarwal, R. V. Ramna, Uttam Singh

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The productivity of a blast furnace and the quality of the hot metal produced are significantly dependent on the smoothness and stability of furnace operation. The permeability of the furnace bed, as well as the gas flow pattern, influences the steady control of process parameters. The softening – melting zone that is formed inside the furnace contributes largely in distribution of the gas flow and the bed permeability. A better shape of softening-melting zone enhances the performance of blast furnace, thereby reducing the fuel rates and improving furnace life. Therefore, predictive model of the softening- melting zone profile can be utilized to control and improve the furnace operation. The shape of softening-melting zone depends upon the physical and chemical properties of the agglomerates and iron ore charged in the furnace. The variations in the agglomerate proportion in the burden at G Blast furnace disturbed the furnace stability. During such circumstances, it was analyzed that a w-shape softening-melting zone profile was formed inside the furnace. The formation of w-shape zone resulted in poor bed permeability and non-uniform gas flow. There was a significant increase in the heat loss at the lower zone of the furnace. The fuel demand increased, and the huge production loss was incurred. Therefore, visibility of softening-melting zone profile was necessary in order to pro-actively optimize the process parameters and thereby to operate the furnace smoothly. Using stave temperatures, a model was developed that predicted the shape of the softening-melting zone inside the furnace. It was observed that furnace operated smoothly during inverse V-shape of the zone and vice-versa during w-shape. This model helped to control the heat loss, optimize the burden distribution and lower the fuel rate at G Blast Furnace, TSL Jamshedpur. As a result of furnace stabilization productivity increased by 10% and fuel rate reduced by 80 kg/thm. Details of the process have been discussed in this paper.

Keywords: agglomerate, blast furnace, permeability, softening-melting

Procedia PDF Downloads 252

Authors: D. S. Naumann, B. J. Evans, O. Hassan

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This paper outlines the development of an automated aerodynamic optimisation algorithm using a novel method of parameterising a computational mesh by employing user–defined control nodes. The shape boundary movement is coupled to the movement of the novel concept of the control nodes via a quasi-1D-linear deformation. Additionally, a second order smoothing step has been integrated to act on the boundary during the mesh movement based on the change in its second derivative. This allows for both linear and non-linear shape transformations dependent on the preference of the user. The domain mesh movement is then coupled to the shape boundary movement via a Delaunay graph mapping. A Modified Cuckoo Search (MCS) algorithm is used for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible NavierStokes solver is used for aerodynamic modelling to predict aerodynamic design fitness. The resulting coupled algorithm is applied to a range of test cases in two dimensions including the design of a subsonic, transonic and supersonic intake and the optimisation approach is compared with more conventional optimisation strategies. Ultimately, the algorithm is tested on a three dimensional wing optimisation case.

Keywords: mesh movement, aerodynamic shape optimization, cuckoo search, shape parameterisation

Procedia PDF Downloads 337

Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Syed Khurram Mahmud, Athar Naveed, Shoaib Arif

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Beamforming is a signal processing technique extensively utilized in wireless communications and radars for desired signal intensification and interference signal minimization through spatial selectivity. In this paper, we present a method for calculation of optimal weight vectors for smart antenna array, to achieve a directive pattern during transmission and selective reception in interference prone environment. In proposed scheme, Mutual Information (MI) extrema are evaluated through an energy constrained objective function, which is based on a-priori information of interference source and desired array factor. Signal to Interference plus Noise Ratio (SINR) performance is evaluated for both transmission and reception. In our scheme, MI is presented as an index to identify trade-off between information gain, SINR, illumination time and spatial selectivity in an energy constrained optimization problem. The employed method yields lesser computational complexity, which is presented through comparative analysis with conventional methods in vogue. MI based beamforming offers enhancement of signal integrity in degraded environment while reducing computational intricacy and correlating key performance indicators.

Keywords: beamforming, interference, mutual information, wireless communications

Procedia PDF Downloads 280

Authors: Felix Boehnisch, Alexander Lutz

Abstract:

Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

Procedia PDF Downloads 182

Authors: Sena Su, Burak Ozbek, Yesim M. Sahin, Sevil Yucel, Dilek Kazan, Faik N. Oktar, Nazmi Ekren, Oguzhan Gunduz

Abstract:

In recent years, protein, lipid or polysaccharide-based polymers have been used in order to develop biodegradable materials and their chemical nature determines the physical properties of the resulting films. Among these polymers, proteins from different sources have been extensively employed because of their relative abundance, film forming ability, and nutritional qualities. In this study, the biodegradable composite nanofiber films based on fish sarcoplasmic protein (FSP) were prepared via electrospinning technique. Biodegradable polycaprolactone (PCL) was blended with the FSP to obtain hybrid FSP/PCL nanofiber mats with desirable physical properties. Mixture solutions of FSP and PCL were produced at different concentrations and their density, viscosity, electrical conductivity and surface tension were measured. Mechanical properties of electrospun nanofibers were evaluated. Morphology of composite nanofibers was observed using scanning electron microscopy (SEM). Moreover, Fourier transform infrared spectrometer (FTIR) studies were used for analysis chemical composition of composite nanofibers. This study revealed that the FSP based nanofibers have the potential to be used for different applications such as biodegradable packaging, drug delivery, and wound dressing, etc.

Keywords: edible film, electrospinning, fish sarcoplasmic protein, nanofiber

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Authors: Fahd I. Alghunaimi, Hind S. Dossary, Norah W. Aljuryyed, Tawfik A. Saleh

Abstract:

Produced water (PW) is one of the largest by-volume waste streams and one of the most challenging effluents in the oil and gas industry. This is due to the variation of contaminants that make up PW. Severalmaterialshavebeen developed, studied, and implemented to remove hydrocarbonsfrom PW. Adsorption is one of the most effective ways ofremoving oil fromPW. In this work, three new and cost-effective hydrophobic adsorbentmaterials based on 9-octadecenoic acid grafted graphene (POG) were synthesized for oil/water separation. Graphene derived from graphite was modified with 9-octadecenoic acid to yield 9-octadecenoic acid grafted graphene (OG). The newsynthesized materials which called POG25, POG50, and POG75 were characterized by using N₂-physisorption (BET) and Fourier transform infrared (FTIR). The BET surface area of POG75 was the highest with 288 m²/g, whereas POG50 was 225 m²/g and POG25 was lowest 79 m²/g. These three materials were also evaluated for their oil-water separation efficiency using a model mixture, whichdemonstrated that POG-75 has the highest oil removal efficiency and the faster rate of the adsorption (Figure-1). POG75 was regenerated, and its performance was verified again with a little reduced adsorption rate compared to the fresh material. The mixtures that used in the performance test were prepared by mixing nonpolar organic liquids such as heptane, dodecane, or hexadecane into the colored water. In general, the new materials showed fast uptake of the certain quantity of the oildue to the high hydrophobicity nature of the materials, which repel water as confirmed by the contact angle of approximately 150˚. Besides that, novel superhydrophobic material was also synthesized by introducing hydrophobic branches of laurate on the surface of the stainless steel mesh (SSM). This novel mesh could help to hold the novel adsorbent materials in a column to remove oil from PW. Both BOG-75 and the novel mesh have the potential to remove oil contaminants from produced water, which will help to provide an opportunity to recover useful components, in addition, to reduce the environmental impact and reuse produced water in several applications such as fracturing.

Keywords: graphite to graphene, oleophilic, produced water, separation

Procedia PDF Downloads 122

Authors: Sergo Esadze

Abstract:

Seismic resistance norms require calculation of cantilevers on vertical components of the base seismic acceleration. Long span cantilevers, as a rule, must be calculated as a separate construction element. According to the architectural-planning solution, functional purposes and environmental condition of a designing buildings/structures, long span cantilever construction may be of very different types: both by main bearing element (beam, truss, slab), and by material (reinforced concrete, steel). A choice from these is always linked with bearing construction system of the building. Research of vertical seismic vibration of these constructions requires individual approach for each (which is not specified in the norms) in correlation with model of seismic load. The latest may be given both as deterministic load and as a random process. Loading model as a random process is more adequate to this problem. In presented paper, two types of long span (from 6m – up to 12m) reinforcement concrete cantilever beams have been considered: a) bearing elements of cantilevers, i.e., elements in which they fixed, have cross-sections with large sizes and cantilevers are made with haunch; b) cantilever beam with load-bearing rod element. Calculation models are suggested, separately for a) and b) types. They are presented as systems with finite quantity degree (concentrated masses) of freedom. Conditions for fixing ends are corresponding with its types. Vertical acceleration and vertical component of the angular acceleration affect masses. Model is based on assumption translator-rotational motion of the building in the vertical plane, caused by vertical seismic acceleration. Seismic accelerations are considered as random processes and presented by multiplication of the deterministic envelope function on stationary random process. Problem is solved within the framework of the correlation theory of random process. Solved numerical examples are given. The method is effective for solving the specific problems.

Keywords: cantilever, random process, seismic load, vertical acceleration

Procedia PDF Downloads 188

Authors: Nam-Hee Kim, Jang-Hoon Ko, Kwon-Hee Lee

Abstract:

A relief valve is a mechanical element to keep safety by controlling high pressure. Usually, the high pressure is relieved by using the spring force and letting the fluid to flow from another way out of system. When its normal pressure is reached, the relief valve can return to initial state. The relief valve in this study has been applied for pressure vessel, evaporator, piping line, etc. The relief valve should be designed for smooth operation and should satisfy the structural safety requirement under operating condition. In general, the structural analysis is performed by following fluid flow analysis. In this process, the FSI (Fluid-Structure Interaction) is required to input the force obtained from the output of the flow analysis. Firstly, this study predicts the velocity profile and the pressure distribution in the given system. In this study, the assumptions for flow analysis are as follows: • The flow is steady-state and three-dimensional. • The fluid is Newtonian and incompressible. • The walls of the pipe and valve are smooth. The flow characteristics in this relief valve does not induce any problem. The commercial software ANSYS/CFX is utilized for flow analysis. On the contrary, very high pressure may cause structural problem due to severe stress. The relief valve is made of body, bonnet, guide, piston and nozzle, and its material is stainless steel. To investigate its structural safety, the worst case loading is considered as the pressure of 700 bar. The load is applied to inside the valve, which is greater than the load obtained from FSI. The maximum stress is calculated as 378 MPa by performing the finite element analysis. However, the value is greater than its allowable value. Thus, an alternative design is suggested to improve the structural performance through case study. We found that the sensitive design variable to the strength is the shape of the nozzle. The case study is to vary the size of the nozzle. Finally, it can be seen that the suggested design satisfy the structural design requirement. The FE analysis is performed by using the commercial software ANSYS/Workbench.

Keywords: relief valve, structural analysis, structural design, strength, safety factor

Procedia PDF Downloads 303