Search results for: subspace rotation algorithm

Authors: Guerich Mohamed, Assaf Samir

Abstract:

The application of viscoelastic materials in the form of constrained layers in mechanical structures is an efficient and cost-effective technique for solving noise and vibration problems. This technique requires a design tool to select the best location, type, and thickness of the damping treatment. This paper presents a finite element model for the vibration of beams partially or fully covered with a constrained viscoelastic damping material. The model is based on Bernoulli-Euler theory for the faces and Timoshenko beam theory for the core. It uses four variables: the through-thickness constant deflection, the axial displacements of the faces, and the bending rotation of the beam. The sandwich beam finite element is compatible with the conventional C1 finite element for homogenous beams. To validate the proposed model, several free vibration analyses of fully or partially covered beams, with different locations of the damping patches and different percent coverage, are studied. The results show that the proposed approach can be used as an effective tool to study the influence of the location and treatment size on the natural frequencies and the associated modal loss factors. Then, a parametric study regarding the variation in the damping characteristics of partially covered beams has been conducted. In these studies, the effect of core shear modulus value, the effect of patch size variation, the thickness of constraining layer, and the core and the locations of the patches are considered. In partial coverage, the spatial distribution of additive damping by using viscoelastic material is as important as the thickness and material properties of the viscoelastic layer and the constraining layer. Indeed, to limit added mass and to attain maximum damping, the damping patches should be placed at optimum locations. These locations are often selected using the modal strain energy indicator. Following this approach, the damping patches are applied over regions of the base structure with the highest modal strain energy to target specific modes of vibration. In the present study, a more efficient indicator is proposed, which consists of placing the damping patches over regions of high energy dissipation through the viscoelastic layer of the fully covered sandwich beam. The presented approach is used in an optimization method to select the best location for the damping patches as well as the material thicknesses and material properties of the layers that will yield optimal damping with the minimum area of coverage.

Keywords: finite element model, damping treatment, viscoelastic materials, sandwich beam

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Authors: Yuriy A. Shevchenko, Konstantin V. Nefedev

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We consider the antiferromagnetic systems of Ising spins located at the sites of the hexagonal, triangular and pyrochlore lattices. Such systems can be diluted to a certain concentration level by randomly replacing the magnetic spins with nonmagnetic ones. Quite recently we studied density of states (DOS) was calculated by the Wang-Landau method. Based on the obtained data, we calculated the dependence of the residual entropy (entropy at a temperature tending to zero) on the dilution concentration for quite large systems (more than 2000 spins). In the current study, we obtained the same data for small systems (less than 20 spins) by a complete search of all possible magnetic configurations and compared the result with the result for large systems. The shape of the curve remains unchanged in both cases, but the specific values of the residual entropy are different because of the finite size effect.

Keywords: entropy, pyrochlore, spin ice, Wang-Landau algorithm

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

Procedia PDF Downloads 328

Authors: K. Roushanger, A. Soleymanzadeh

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Weirs are structures often used in irrigation techniques, sewer networks and flood protection. However, the hydraulic behavior of this type of weir is complex and difficult to predict accurately. An accurate flow prediction over a weir mainly depends on the proper estimation of discharge coefficient. In this study, the Genetic Expression Programming (GEP) approach was used for predicting trapezoidal and rectangular sharp-crested side weirs discharge coefficient. Three different performance indexes are used as comparing criteria for the evaluation of the model’s performances. The obtained results approved capability of GEP in prediction of trapezoidal and rectangular side weirs discharge coefficient. The results also revealed the influence of downstream Froude number for trapezoidal weir and upstream Froude number for rectangular weir in prediction of the discharge coefficient for both of side weirs.

Keywords: discharge coefficient, genetic expression programming, trapezoidal weir

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Authors: Mohammad Ghavami, Reza S. Dilmaghani

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This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

Procedia PDF Downloads 336

Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Boukhris Lahouari, Bouchetara Mostefa

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The active safety of a vehicle is mainly influenced by the properties of the installed braking system. With the increase in road traffic density and travel speeds, increasingly stringent requirements are placed on the vehicle's behaviour during braking. The achievable decelerations are limited by the physical aspect characterized by the coefficient of friction between the tires and the ground. As a result, it follows that an optimized distribution of braking forces becomes necessary for a better use of friction coefficients. This objective could only be achieved if sufficient knowledge is available on the theory of vehicle dynamics during braking and on current standards for the approval of braking systems. This will facilitate the development of a braking force calculation algorithm that will enable an optimized distribution of braking forces to be achieved. Operating safety is conditioned by the requirements of efficiency, progressiveness, regularity or fidelity of a braking system without obviously neglecting the recommendations imposed by the legislator.

Keywords: brake force distribution, distribution diagram, friction coefficient, brake by wire

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Authors: F. Karim

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In this paper, triangular lattice index-guiding photonic crystal fibers (PCFs) are synthesized to compensate the chromatic dispersion of a single mode fiber (SMF-28) for an 80 km optical link operating at 1.55 µm, by using the directed tabu search algorithm. Hole-to-hole distance, circular air-hole diameter, solid-core diameter, ring number and PCF length parameters are optimized for this purpose. Three Synthesized PCFs with different physical parameters are compared in terms of their objective functions values, residual dispersions and compensation ratios.

Keywords: triangular lattice index-guiding photonic crystal fiber, dispersion compensation, directed tabu search, synthesis

Procedia PDF Downloads 432

Authors: I. Bentchikou, A. Tlemcani, F. Boudjema, D. Boukhetala, N. Ould Cherchali

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In this paper, hybrid approach is proposed to regulate the voltages of the floating capacitor multicell inverter and the current in the load. This structure makes it possible to ensure the distribution of the voltage stresses on the various low-voltage semiconductor components connected in series. And as the problem and to keep a constant voltage across the capacitors. Thus, it is necessary to ensure a distribution balanced voltages at the terminals of floating capacitors thanks to Algorithm develop for this, using the Petri nets. So we consider a three-cell converter represented as a hybrid system with eight modes of operation. The operating modes of the system are governed by the control reference voltage and a reference current. Finally, we present the results of the simulation with MATLAB/SIMULINK to illustrate the performances of this approach.

Keywords: hybrid control, floating condensers, multicellular converter, petri nets

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Authors: Ronald Croce, Timothy Quinn, John Miller

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Incomplete activation, or activation failure, of motor units during maximal voluntary contractions is often referred to as muscle inhibition (MI), and is defined as the inability of the central nervous system to maximally drive a muscle during a voluntary contraction. The purpose of the present study was to assess the interrelationship amongst peak torque (PT), muscle inhibition (MI; incomplete activation of motor units), and voluntary muscle activation (VMA) of the quadriceps’ muscle group as a function of knee angle and muscle length during maximal voluntary isometric contractions (MVICs). Nine young adult males (mean + standard deviation: age: 21.58 + 1.30 years; height: 180.07 + 4.99 cm; weight: 89.07 + 7.55 kg) performed MVICs in random order with the knee at 15, 55, and 95° flexion. MI was assessed using the interpolated twitch technique and was estimated by the amount of additional knee extensor PT evoked by the superimposed twitch during MVICs. Voluntary muscle activation was estimated by root mean square amplitude electromyography (EMGrms) and mechanomyography (MMGrms) of agonist (vastus medialis [VM], vastus lateralis [VL], and rectus femoris [RF]) and antagonist (biceps femoris ([BF]) muscles during MVICs. Data were analyzed using separate repeated measures analysis of variance. Results revealed a strong dependency of quadriceps’ PT (p < 0.001), MI (p < 0.001) and MA (p < 0.01) on knee joint position: PT was smallest at the most shortened muscle position (15°) and greatest at mid-position (55°); MI and MA were smallest at the most shortened muscle position (15°) and greatest at the most lengthened position (95°), with the RF showing the greatest change in MA. It is hypothesized that the ability to more fully activate the quadriceps at short compared to longer muscle lengths (96% contracted at 15°; 91% at 55°; 90% at 95°) might partly compensate for the unfavorable force-length mechanics at the more extended position and consequent declines in VMA (decreases in EMGrms and MMGrms muscle amplitude during MVICs) and force production (PT = 111-Nm at 15°, 217-NM at 55°, 199-Nm at 95°). Biceps femoris EMG and MMG data showed no statistical differences (p = 0.11 and 0.12, respectively) at joint angles tested, although there were greater values at the extended position. Increased BF muscle amplitude at this position could be a mechanism by which anterior shear and tibial rotation induced by high quadriceps’ activity are countered. Measuring and understanding the degree to which one sees MI and VMA in the QF muscle has particular clinical relevance because different knee-joint disorders, such ligament injuries or osteoarthritis, increase levels of MI observed and markedly reduced the capability of full VMA.

Keywords: electromyography, interpolated twitch technique, mechanomyography, muscle activation, muscle inhibition

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Kelaiaia Mounia Samira, Labar Hocine, Mesbah Tarek, Kelaiaia samia

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The photovoltaic panel produces green power, and because of its availability across the globe, it can supply isolated loads (site away of the electrical network or difficult of access). Unfortunately this energy remains very expensive. The most application of these types of power needs storage devices, the Lithium batteries are commonly used because of its powerful storage capability. Using a solar panel or an array of panels without a controller that can perform MPPT will often result in wasted power, which results in the need to install more panels for the same power requirement. For devices that have the battery connected directly to the panel, this will also result in premature battery failure or capacity loss. In this paper it is proposed a modified P&O algorithm for the MPPT which takes in account the battery’s internal resistance vs temperature and stage of charging. Of course the temperature variation and irradiation of the PV panel are also introduced.

Keywords: modeling, battery, MPPT, charging, PV Panel

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Authors: Zhenqiu Liu

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Visualizing the heterogeneity within cell-populations for single-cell RNA-seq data is crucial for studying the functional diversity of a cell. However, because of the high level of noises, outlier, and dropouts, it is very challenging to measure the cell-to-cell similarity (distance), visualize and cluster the data in a low-dimension. Minimum volume embedding (MVE) projects the data into a lower-dimensional space and is a promising tool for data visualization. However, it is computationally inefficient to solve a semi-definite programming (SDP) when the sample size is large. Therefore, it is not applicable to single-cell RNA-seq data with thousands of samples. In this paper, we develop an efficient algorithm with an accelerated proximal gradient method and visualize the single-cell RNA-seq data efficiently. We demonstrate that the proposed approach separates known subpopulations more accurately in single-cell data sets than other existing dimension reduction methods.

Keywords: single-cell RNA-seq, minimum volume embedding, visualization, accelerated proximal gradient method

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Authors: Charalampos Saridakis, Stelios Tsafarakis

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Although environmental concern is on the rise across Europe, current market data indicate that adoption rates of environmentally friendly vehicles remain extremely low. Against this background, the aim of this paper is to a) assess preferences of European consumers for clean-fuel cars and their characteristics and b) design car lines that optimize the combination of fuel types among models in the line-up. In this direction, the authors introduce a new evolutionary mechanism and implement it to stated-preference data derived from a large-scale choice-based conjoint experiment that measures consumer preferences for various factors affecting clean-fuel vehicle (CFV) adoption. The proposed two-step methodology provides interesting insights into how new and existing fuel-types can be combined in a car line that maximizes customer satisfaction.

Keywords: clean-fuel vehicles, product line design, conjoint analysis, choice experiment, differential evolution

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Authors: Jessica A. Soto-Bear, Virginia González-Vélez, Norma Castañeda-Villa, Amparo Gil

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Glutamate release of the cochlear inner hair cell (IHC) ribbon synapse is a fundamental step in transferring sound information in the auditory pathway. Otoferlin is the calcium sensor in the IHC and its activity has been related to many auditory disorders. In order to simulate secretion dynamics occurring in the IHC in a few milliseconds timescale and with high spatial resolution, we proposed an active-zone model solved with Monte Carlo algorithms. We included models for calcium buffered diffusion, calcium-binding schemes for vesicle fusion, and L-type voltage-gated calcium channels. Our results indicate that calcium influx and calcium binding is managing IHC secretion as a function of voltage depolarization, which in turn mean that IHC response depends on sound intensity.

Keywords: inner hair cells, Monte Carlo algorithm, Otoferlin, secretion

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Authors: Douglas N. Simoes, Michele Pittol, Vanda F. Ribeiro, Daiane Tomacheski, Ruth M. C. Santana

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Thermoplastic elastomers (TPE) compounds are used in a wide range of applications, like home appliances, automotive components, medical devices, footwear, and others. These materials are susceptible to microbial attack, causing a crack in polymer chains compounds based on SEBS copolymers, poly (styrene-b-(ethylene-co-butylene)-b-styrene, are a class of TPE, largely used in domestic appliances like refrigerator seals (gaskets), bath mats and sink squeegee. Moisture present in some areas (such as shower area and sink) in addition to organic matter provides favorable conditions for microbial survival and proliferation, contributing to the spread of diseases besides the reduction of product life cycle due the biodegradation process. Zinc oxide (ZnO) has been studied as an alternative antibacterial additive due its biocidal effect. It is important to know the influence of these additives in the properties of the compounds, both at the beginning and during the life cycle. In that sense, the aim of this study was to evaluate the effect of accelerated aging in oven on antibacterial and mechanical properties of ZnO loaded SEBS based TPE compounds. Two different comercial zinc oxide, named as WR and Pe were used in proportion of 1%. A compound with no antimicrobial additive (standard) was also tested. The compounds were prepared using a co-rotating double screw extruder (L/D ratio of 40/1 and 16 mm screw diameter). The extrusion parameters were kept constant for all materials, screw rotation rate was set at 226 rpm, with a temperature profile from 150 to 190 ºC. Test specimens were prepared using the injection molding machine at 190 ºC. The Standard Test Method for Rubber Property—Effect of Liquids was applied in order to simulate the exposition of TPE samples to detergent ingredients during service. For this purpose, ZnO loaded TPE samples were immersed in a 3.0% w/v detergent (neutral) and accelerated aging in oven at 70°C for 7 days. Compounds were characterized by changes in mechanical (hardness and tension properties) and mass. The Japan Industrial Standard (JIS) Z 2801:2010 was applied to evaluate antibacterial properties against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The microbiological tests showed a reduction up to 42% in E. coli and up to 49% in S. aureus population in non-aged samples. There were observed variations in elongation and hardness values with the addition of zinc The changes in tensile at rupture and mass were not significant between non-aged and aged samples.

Keywords: antimicrobial, domestic appliance, sebs, zinc oxide

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Richard Tanaka, Ying Zhu

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This paper studies a few randomized algorithms in application-layer peer-to-peer networks. The significant gain in scalability and resilience that peer-to-peer networks provide has made them widely used and adopted in many real-world distributed systems and applications. The unique properties of peer-to-peer networks make them particularly suitable for randomized algorithms such as random walks and gossip algorithms. Instead of simulations of peer-to-peer networks, we leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and these distributed randomized algorithms running on top of them. We can thus analyze their behaviour and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations.

Keywords: distributed randomized algorithms, peer-to-peer networks, virtual container technology, resilient networks

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Authors: S. Ghorbani, N. I. Polushin

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Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang.

Keywords: cutting condition, vibration, natural frequency, decision tree, CART algorithm

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Authors: Yi F. Wu, Ai Q. Li, Hao Wang

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Taking a novel type of bridge bearings with the diameter being 100mm as an example, the theoretical analysis, the experimental research as well as the numerical simulation of the bearing were conducted. Since the normal compression-shear machines cannot be applied to the small-size bearing, an improved device to test the properties of the bearing was proposed and fabricated. Besides, the simulation of the bearing was conducted on the basis of the explicit finite element software ANSYS/LS-DYNA, and some parameters of the bearing are modified in the finite element model to effectively reduce the computation cost. Results show that all the research methods are capable of revealing the fundamental properties of the small-size bearings, and a combined use of these methods can better catch both the integral properties and the inner detailed mechanical behaviors of the bearing.

Keywords: ANSYS/LS-DYNA, compression shear, contact analysis, explicit algorithm, small-size

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Authors: Danilo López, Edwin Rivas, Fernando Pedraza

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Currently, one of the major challenges in wireless networks is the optimal use of radio spectrum, which is managed inefficiently. One of the solutions to existing problem converges in the use of Cognitive Radio (CR), as an essential parameter so that the use of the available licensed spectrum is possible (by secondary users), well above the usage values that are currently detected; thus allowing the opportunistic use of the channel in the absence of primary users (PU). This article presents the results found when estimating or predicting the future use of a spectral transmission band (from the perspective of the PU) for a chaotic type channel arrival behavior. The time series prediction method (which the PU represents) used is ANFIS (Adaptive Neuro Fuzzy Inference System). The results obtained were compared to those delivered by the RNA (Artificial Neural Network) algorithm. The results show better performance in the characterization (modeling and prediction) with the ANFIS methodology.

Keywords: ANFIS, cognitive radio, prediction primary user, RNA

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Authors: Nikhat Nawaz, S. Srinivasulu, P. Kesava Rao

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Feature extraction plays an important role in many remote sensing applications. Automatic extraction of water bodies is of great significance in many remote sensing applications like change detection, image retrieval etc. This paper presents a procedure for automatic extraction of water information from remote sensing images. The algorithm uses the relative location of R-colour component of the chromaticity diagram. This method is then integrated with the effectiveness of the spatial scale transformation of whole method. The whole method is based on water index fitted from spectral library. Experimental results demonstrate the improved accuracy and effectiveness of the integrated method for automatic extraction of water bodies.

Keywords: feature extraction, remote sensing, image retrieval, chromaticity, water index, spectral library, integrated method

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Authors: Mohamed Barbary, Mohamed H. Abd El-azeem

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Maneuvering non-ellipsoidal extended object tracking (M-NEOT) using high-resolution inverse synthetic aperture radar (ISAR) observations is gaining momentum recently. This work presents a new robust implementation of the Jump Markov (JM) multi-Bernoulli (MB) filter for M-NEOT, where the M-NEOT’s ISAR observations are characterized using a skewed (SK) non-symmetrically normal distribution. To cope with the possible abrupt change of kinematic state, extension, and observation distribution over an extended object when a target maneuvers, a multiple model technique is represented based on an MB-track-before-detect (TBD) filter supported by SK-sub-random matrix model (RMM) or sub-ellipses framework. Simulation results demonstrate this remarkable impact.

Keywords: maneuvering extended objects, ISAR, skewed normal distribution, sub-RMM, JM-MB-TBD filter

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Authors: Atef Ibrahim, Hamed Elsimary, Abdullah Aljumah, Fayez Gebali

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This paper presents a novel semi-systolic array architecture for an optimized parallel sequence alignment algorithm. This architecture has the advantage that it can be modified to be reused for multiple pass processing in order to increase the number of processing elements that can be packed into a single FPGA and to increase the number of sequences that can be aligned in parallel in a single FPGA. This resolves the potential problem of many FPGA resources left unused for designs that have large values of short read length. When using the previously published conventional hardware design. FPGA implementation results show that, for large values of short read lengths (M>128), the proposed design has a slightly higher speed up and FPGA utilization over the the conventional one.

Keywords: bioinformatics, genome sequence alignment, re-sequencing applications, systolic array

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Authors: Kapila Warnakulasuriya, Walimuni Janaka Mendis

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In this study, an artificial intelligence-based approach is developed to diagnose abnormal tissues in human or animal bodies by analyzing magnetic resonance signals. As opposed to the conventional method of generating an image from the magnetic resonance signals, which are then evaluated by a radiologist for the diagnosis of abnormalities, in the discussed approach, the magnetic resonance signals are analyzed by an artificial intelligence algorithm without having to generate or analyze an image. The AI-based program compares magnetic resonance signals with millions of possible magnetic resonance waveforms which can be generated from various types of normal tissues. Waveforms generated by abnormal tissues are then identified, and images of the abnormal tissues are generated with the possible location of them in the body for further diagnostic tests.

Keywords: magnetic resonance, artificial intelligence, magnetic waveform analysis, abnormal tissues

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Authors: Henrry Vicente Rojas Asuero, Holger Manuel Benavides Muñoz

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Approximately 80% of the energy consumed worldwide is generated from fossil sources, which are responsible for the emission of a large volume of greenhouse gases. For this reason, the global trend, at present, is the widespread use of energy produced from renewable sources. This seeks safety and diversification of energy supply, based on social cohesion, economic feasibility and environmental protection. In this scenario, small hydropower systems (P ≤ 10MW) stand out due to their high efficiency, economic competitiveness and low environmental impact. Small hydropower systems, along with wind and solar energy, are expected to represent a significant percentage of the world's energy matrix in the near term. Among the various technologies present in the state of the art, relating to small hydropower systems, is the Gravitational Water Vortex Power Plant, a recent technology that excels because of its versatility of operation, since it can operate with jumps in the range of 0.70 m-2.00 m and flow rates from 1 m3/s to 20 m3/s. Its operating system is based on the utilization of the energy of rotation contained within a large water vortex artificially induced. This paper presents the study and experimental design of an optimal hydraulic structure with the capacity to induce the artificial formation of a gravitational water vortex trough a system of easy application and high efficiency, able to operate in conditions of very low head and minimum flow. The proposed structure consists of a channel, with variable base, vortex inductor, tangential flow generator, coupled to a circular tank with a conical transition bottom hole. In the laboratory test, the angular velocity of the water vortex was related to the geometric characteristics of the inductor channel, as well as the influence of the conical transition bottom hole on the physical characteristics of the water vortex. The results show angular velocity values of greater magnitude as a function of depth, in addition the presence of the conical transition in the bottom hole of the circular tank improves the water vortex formation conditions while increasing the angular velocity values. Thus, the proposed system is a sustainable solution for the energy supply of rural areas near to watercourses.

Keywords: experimental model, gravitational water vortex power plant, renewable energy, small hydropower

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Authors: Jung–Min Yang

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Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the desired input/output behavior. A matrix expression is presented to address reachability of switched asynchronous sequential machines with output equivalence with respect to a model. The presented reachability condition for the controller design is validated in a simple example.

Keywords: asynchronous sequential machines, corrective control, model matching, input/output control

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Authors: Amira Dhokar, Lobna Hlaoua, Lotfi Ben Romdhane

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Tweet contextualization (TC) is a new issue that aims to answer questions of the form 'What is this tweet about?' The idea of this task was imagined as an extension of a previous area called multi-document summarization (MDS), which consists in generating a summary from many sources. In both TC and MDS, the summary should ideally contain the most relevant information of the topic that is being discussed in the source texts (for MDS) and related to the query (for TC). Furthermore of being informative, a summary should be coherent, i.e. well written to be readable and grammatically compact. Hence, coherence is an essential characteristic in order to produce comprehensible texts. In this paper, we propose a new approach to improve readability and coherence for tweet contextualization based on bipartite graphs. The main idea of our proposed method is to reorder sentences in a given paragraph by combining most expressive words detection and HITS (Hyperlink-Induced Topic Search) algorithm to make up a coherent context.

Keywords: bipartite graphs, readability, summarization, tweet contextualization

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Authors: Ng Liang Shen, Ngahzaifa Abdul Ghani

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This research aimed at developing an early warning system for pre-diabetic and diabetics by analyzing simple and easily determinable signs and symptoms of diabetes among the people living in Malaysia using Particle Swarm Optimized Artificial. With the skyrocketing prevalence of Type 2 diabetes in Malaysia, the system can be used to encourage affected people to seek further medical attention to prevent the onset of diabetes or start managing it early enough to avoid the associated complications. The study sought to find out the best predictive variables of Type 2 Diabetes Mellitus, developed a system to diagnose diabetes from the variables using Artificial Neural Networks and tested the system on accuracy to find out the patent generated from diabetes diagnosis result in machine learning algorithms even at primary or advanced stages.

Keywords: diabetes diagnosis, Artificial Neural Networks, artificial intelligence, soft computing, medical diagnosis

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Authors: Yuzhi Cai

Abstract:

This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

Procedia PDF Downloads 347