Search results for: neural electrodes

Authors: Jiangbo Pu, Hanhui Xu, Yazhou Wang, Hongyan Cui, Yong Hu

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Spinal cord injury (SCI) brings great negative influence to the patients and society. Neurological loss in human after SCI is a major challenge in clinical. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. Here we used sample entropy as an indicator of nonlinear dynamical in the brain to quantify plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the entropy values were increased after the injury during the recovery in one week. The increasing tendency of sample entropy values is consistent with that of behavioral evaluation scores. It is indicated the potential application of sample entropy analysis for the evaluation of neural plasticity in spinal cord injury rat model.

Keywords: spinal cord injury (SCI), sample entropy, nonlinear, complex system, firing pattern, EEG, spontaneous activity, Basso Beattie Bresnahan (BBB) score

Procedia PDF Downloads 465

Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

Procedia PDF Downloads 836

Authors: Margarita Mayoral-Villa, J. Klapp, L. Di G. Sigalotti, J. E. V. Guzmán

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Automated learning techniques are widely applied in the energy sector to address challenging problems from a practical point of view. To this end, we discuss the implementation of a Message Passing algorithm (MPNN)within a Graph Neural Network(GNN)to leverage the neighborhood of a set of nodes during the aggregation process. This approach enables the characterization of multiphase diffusion processes in the reservoir, such that the flow paths underlying the interconnections between multiple wells may be inferred from previously available data on flow rates and bottomhole pressures. The results thus obtained compare favorably with the predictions produced by the Reduced Order Capacitance-Resistance Models (CRM) and suggest the potential of MPNNs to enhance the robustness of the forecasts while improving the computational efficiency.

Keywords: multiphase diffusion, message passing neural network, well interconnection, interwell connectivity, graph neural network, capacitance-resistance models

Procedia PDF Downloads 149

Authors: Malory Jonata

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Chlordecone (CLD) is an organochlorine pesticide used between 1971 and 1993 in both Guadeloupe and Martinique for the control of banana black weevil. The bishomocubane structure which characterizes this chemical compound led to high stability in organic matter and high persistence in the environment. Recently, researchers found that CLD can be degraded by isolated bacteria consortiums and, particularly, by bacteria such as Citrobacter sp 86 and Delsulfovibrio sp 86. Actually, six transformation product families of CLD are known. Moreover, the latest discovery showed that CLD was disappearing faster than first predicted in highly contaminated soil in Guadeloupe. However, the toxicity of transformation products is still unknown, and knowledge has to be deepened on the degradation ways and chemical characteristics of chlordecone and its transformation products. Microbial fuel cells (MFC) are electrochemical systems that can convert organic matter into electricity thanks to electroactive bacteria. These bacteria can exchange electrons through their membranes to solid surfaces or molecules. MFC have proven their efficiency as bioremediation systems in water and soils. They are already used for the bioremediation of several organochlorine compounds such as perchlorate, trichlorophenol or hexachlorobenzene. In this study, a three-electrodes system, inspired by MFC, is used to try to degrade chlordecone using bacteria from a mangrove swamp in Martinique. As we know, some mangrove bacteria are electroactive. Furthermore, the CLD rate seems to decline in mangrove swamp sediments. This study aims to prove that electroactive bacteria from a mangrove swamp in Martinique can degrade CLD thanks to a three-electrodes bioelectrochemical system. To achieve this goal, the tree-electrodes assembly has been connected to a potentiostat. The substrate used is mangrove water and sediments sampled in the mangrove swamp of La Trinité, a coastal city in Martinique, where CLD contamination has already been studied. Electroactive biofilms are formed by imposing a potential relative to Saturated Calomel Electrode using chronoamperometry. Moreover, their comportment has been studied by using cyclic voltametry. Biofilms have been studied under different imposed potentials, several conditions of the substrate and with or without CLD. In order to quantify the evolution of CLD rates in the substrate’s system, gas chromatography coupled with mass spectrometry (GC-MS) was performed on pre-treated samples of water and sediments after short, medium and long-term contact with the electroactive biofilms. Results showed that between -0,8V and -0,2V, the three-electrodes system was able to reduce the chemical in the substrate solution. The first GC-MS analysis result of samples spiked with CLD seems to reveal decreased CLD concentration over time. In conclusion, the designed bioelectrochemical system can provide the necessary conditions for chlordecone degradation. However, it is necessary to improve three-electrodes control settings in order to increase degradation rates. The biological pathways are yet to enlighten by biologicals analysis of electroactive biofilms formed in this system. Moreover, the electrochemical study of mangrove substrate gives new informations on the potential use of this substrate for bioremediation. But further studies are needed to a better understanding of the electrochemical potential of this environment.

Keywords: bioelectrochemistry, bioremediation, chlordecone, mangrove swamp

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Authors: Qingying Liu, S. Liu, L. Hao, B. Zhang, J. D. Yan

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HVDC technology is becoming increasingly popular due to its simplicity in topology and less power loss over long distance of power transmission, in comparison with HVAC technology. However, the dielectric behavior of insulators in the long term under HVDC stress is completely different from that under HVAC stress as a result of charge accumulation in a constant electric field. Insulators used in practical systems are never perfect in their structural conditions. Over time shallow cracks may develop on their surface. The presence of defects can lead to drastic change in their dielectric behaviour and thus increase the probability of surface flashover. In this contribution, experimental investigations have been carried out on the charge accumulation phenomenon on the surface of a rod insulator made of epoxy that is placed between two disk shaped electrodes at different voltage levels and in different gases (SF6, CO2 and N2). Many results obtained, such as, the two-dimensional electrostatic potential distribution along the insulator surface after the removal of the power source following a pre-defined period of application. The probe has been carefully calibrated before each test. Results show that surface charge distribution near the two disk shaped electrodes is not uniform in the circumferential direction, possibly due to the imperfect electrical connections between the embeded conductor in the insulator and the disk shaped electrodes. The axial length of this non-uniform region is experimentally determined, which provides useful information for shielding design. A charge transport model is also used to explain the formation of the long term electrostatic potential distribution under a constant applied voltage.

Keywords: HVDC, power systems, dielectric behavior, insulation, charge accumulation

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

Procedia PDF Downloads 231

Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Yasaman Mohammadi

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Emotion regulation is a complex process that allows individuals to manage and modulate their emotional responses in order to adaptively respond to environmental demands. As individuals age, emotion regulation abilities may decline, leading to an increased vulnerability to mood disorders and other negative health outcomes. Advances in neuroimaging techniques have greatly enhanced our understanding of the neural substrates underlying emotion regulation and age-related changes in these neural systems. Additionally, genetic research has identified several candidate genes that may influence age-related changes in emotion regulation. In this paper, we review recent findings from neuroimaging and genetic research on age-related changes in the neural substrates of emotion regulation, highlighting the mechanisms and consequences of these changes. We also discuss potential interventions, including cognitive and behavioral approaches, that may be effective in mitigating age-related declines in emotion regulation. We propose that a better understanding of the mechanisms underlying age-related changes in emotion regulation may lead to the development of more targeted interventions aimed at promoting healthy emotional functioning in older adults. Overall, this paper highlights the importance of studying age-related changes in emotion regulation and provides a roadmap for future research in this field.

Keywords: emotion regulation, aging, neural substrates, neuroimaging, emotional functioning, healthy aging

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Authors: Oluwaponmile David Alao

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Due to an increase in the death rate as a result of heart attack. There is need to develop a system that can be useful in the diagnosis of the disease at the medical centre. This system will help in preventing misdiagnosis that may occur from the medical practitioner or the physicians. In this research work, heart disease dataset obtained from UCI repository has been used to develop an intelligent prediction diagnosis system. The system is modeled on a feedforwad neural network and trained with back propagation neural network. A recognition rate of 86% is obtained from the testing of the network.

Keywords: heart disease, artificial neural network, diagnosis, prediction system

Procedia PDF Downloads 450

Authors: Ruey-Chi Wang, Po-Hsiang Huang, Ping-Chang Chuang, Shu-Jen Chen

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High-performance Li: CuO/reduced graphene oxide (RGO) flexible electrodes for supercapacitors were fabricated via a low-temperature and low-cost route. To increase energy density while maintaining high power density and long-term cyclability, Li was doped to increase the electrical conductivity of CuO particles between RGO flakes. Electrochemical measurements show that the electrical conductivity, specific capacitance, energy density, and rate capability were all enhanced by Li incorporation. The optimized Li:CuO/RGO electrodes show a high energy density of 179.9 Wh/kg and a power density of 900.0 W/kg at a current density of 1 A/g. Cyclic life tests show excellent stability over 10,000 cycles with a capacitance retention of 93.2%. Li doping improves the electrochemical performance of CuO, making CuO a promising pseudocapacitive material for fabricating low-cost excellent supercapacitors.

Keywords: supercapacitor, CuO, RGO, lithium

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Authors: Ahmad Manbohi, Seyyed Hamid Ahmadi

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A low-cost paper-based microfluidic device (PAD) for the multiplex electrochemical determination of glucose, uric acid, and dopamine in biological fluids was developed. Using wax printing, PAD containing a central zone, six channels, and six detection zones was fabricated, and the electrodes were printed on detection zones using pre-made electrodes template. For each analyte, two detection zones were used. The carbon working electrode was coated with chitosan-BSA (and enzymes for glucose and uric acid). To detect glucose and uric acid, enzymatic reactions were employed. These reactions involve enzyme-catalyzed redox reactions of the analytes and produce free electrons for electrochemical measurement. Calibration curves were linear (R^² > 0.980) in the range of 0-80 mM for glucose, 0.09–0.9 mM for dopamine, and 0–50 mM for uric acid, respectively. Blood samples were successfully analyzed by the proposed method.

Keywords: biological fluids, biomarkers, microfluidic paper-based electrochemical biosensors, Multiplex

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Authors: Himanshu J. Bahirat, Apoorva S. Janawlekar

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Plasma actuators are essential for aerodynamic flow separation control due to their lack of mechanical parts, lightweight, and high response frequency, which have numerous applications in hypersonic or supersonic aircraft. The working of these actuators is based on the formation of a low-temperature plasma between a pair of parallel electrodes by the application of a high-voltage AC signal across the electrodes, after which air molecules from the air surrounding the electrodes are ionized and accelerated through the electric field. The high-frequency operation is required in dielectric discharge barriers to ensure plasma stability. To carry out flow separation control in a hypersonic flow, the optimal design and construction of a power supply to generate dielectric barrier discharges is carried out in this paper. In this paper, it is aspired to construct a simplified circuit topology to emulate the dielectric barrier discharge and study its various frequency responses. The power supply can generate high voltage pulses up to 20kV at the repetitive frequency range of 20-50kHz with an input power of 500W. The power supply has been designed to be short circuit proof and can endure variable plasma load conditions. Its general outline is to charge a capacitor through a half-bridge converter and then later discharge it through a step-up transformer at a high frequency in order to generate high voltage pulses. After simulating the circuit, the PCB design and, eventually, lab tests are carried out to study its effectiveness in controlling flow separation.

Keywords: aircraft propulsion, dielectric barrier discharge, flow separation control, power source

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Gabriel Wosiak, Felipe Staciaki, Eryka Nobrega, Ernesto Pereira

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Hydrogen is an energy carrier with potential applications in various industries. Alkaline electrolysis is a commonly used method for hydrogen production; however, its energy cost remains relatively high compared to other methods. This is due in part to interfacial pH changes that occur during the electrolysis process. Interfacial pH changes refer to the changes in pH that occur at the interface between the cathode electrode and the electrolyte solution. These changes are caused by the electrochemical reactions at both electrodes, which consume or produces hydroxide ions (OH-) from the electrolyte solution. This results in an important change in the local pH at the electrode surface, which can have several impacts on the energy consumption and durability of electrolysers. One impact of interfacial pH changes is an increase in the overpotential required for hydrogen production. Overpotential is the difference between the theoretical potential required for a reaction to occur and the actual potential that is applied to the electrodes. In the case of water electrolysis, the overpotential is caused by a number of factors, including the mass transport of reactants and products to and from the electrodes, the kinetics of the electrochemical reactions, and the interfacial pH. An increase in the interfacial pH at the anode surface in alkaline conditions can lead to an increase in the overpotential for hydrogen production. This is because the lower local pH makes it more difficult for the hydroxide ions to be oxidized. As a result, there is an increase in the required energy to the process occur. In addition to increasing the overpotential, interfacial pH changes can also lead to the degradation of the electrodes. This is because the lower pH can make the electrode more susceptible to corrosion. As a result, the electrodes may need to be replaced more frequently, which can increase the overall cost of water electrolysis. The method presented in the paper addresses the issue of interfacial pH changes by using a cell design with a different cell design, introducing the electrode asymmetry. This design helps to mitigate the pH gradient at the anode/electrolyte interface, which reduces the overpotential and improves the energy efficiency of the electrolyser. The method was tested using a multivariate approach in both laboratory and industrial current density conditions and validated the results with numerical simulations. The results demonstrated a clear improvement (11.6%) in energy efficiency, providing an important contribution to the field of sustainable energy production. The findings of the paper have important implications for the development of cost-effective and sustainable hydrogen production methods. By mitigating interfacial pH changes, it is possible to improve the energy efficiency of alkaline electrolysis and make it a more competitive option for hydrogen production.

Keywords: electrolyser, interfacial pH, numerical simulation, optimization, asymmetric cell

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Authors: Abdelazziz Aouiche, El Moundher Aouiche, Mouhamed Salah Soudani

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Renewable energy sources now significantly contribute to the replacement of traditional fossil fuel energy sources. One of the most potent types of renewable energy that has developed quickly in recent years is photovoltaic energy. We all know that solar energy, which is sustainable and non-depleting, is the best knowledge form of energy that we have at our disposal. Due to changing weather conditions, the primary drawback of conventional solar PV cells is their inability to track their maximum power point. In this study, we apply artificial neural networks (ANN) to automatically track and measure the maximum power point (MPP) of solar panels. In MATLAB, the complete system is simulated, and the results are adjusted for the external environment. The results are better performance than traditional MPPT methods and the results demonstrate the advantages of using neural networks in solar PV systems.

Keywords: modeling, photovoltaic panel, artificial neural networks, maximum power point tracking

Procedia PDF Downloads 89

Authors: Tam D. Nguyen, Joe Varga, Douglas MacFarlane, Alexandr Simonov

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Development of advanced technologies for green hydrogen generation from renewables is of key strategic importance to global future energy security and economic growth. Renewable-powered water electrolysis (WE) is considered as the most effective of the sustainable methods for hydrogen generation at scale. Currently, the greatest challenge of hydrogen production via water electrolysis is the insufficiently high efficiency. In which, the energy loss associated with the conversion of water to hydrogen is approximately 40-60%, with 30-35% associated with the electrolysis itself and 10-12% with gas compression and transportation. Hence, development of an energy-efficient water electrolyser that can generate hydrogen at high pressure will address both of these major challenges. This requires the development of advanced electrode configuration of the water electrolysis cell. Herein, we developed a highly-ordered interconnected structure of the metallic inverse-opal (IO) frameworks based on low cost materials, e.g. Cu, Ni, Fe, Co. The water electrolysis electrodes based on these frameworks can provide excellent mechanical strength required for the application under conditions of extreme pressure, as well as outstanding catalytic performance through the exceptional high surface area and high electrical conductivity. For example, NiFe layered double hydroxide (LDH) catalyst deposited on Cu IO is able to reach the oxygen evolution reaction (OER) catalytic performance up to the rates of > 100 mA cm−2 (>727A gcatalyst-1) at an overpotential of ~0.3 V. This high performance is achieved with only few micron-thick catalyst layers, in contrast to similarly performance of 103-fold thicker electrodes based on foams and other substrates.

Keywords: oxygen evolution reaction, support materials, mass transport, NiFe LDH

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Authors: Chung-Yu Wu, Chia-Chi Chang, Wei-Ming Chen, Pu-Wei Wu, Shih-Fan Chen, Po-Chun Chen

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Electrostimulation medical devices for neural diseases require electroactive and biocompatible materials to transmit signals from electrodes to targeting tissues. Protection of surrounding tissues has become a great challenge for long-term implants. In this study, we designed back-end processes with compatible, efficient, and reliable advantages over the current state-of-the-art. We explored a hermetic packaging process with high quality of adhesion and uniformity as the biocompatible devices for long-term implantation. This approach is able to provide both excellent biocompatibility and protection to the biomedical electronic devices by performing conformal coating of biocompatible materials. We successfully developed a packaging process that is capable of exposing the stimulating electrode and cover all other faces of chip with high quality of protection to prevent leakage of devices and body fluid.

Keywords: biocompatible package, medical microelectronics, surface coating, long-term implantation

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Authors: Hui Wei, Zheng Dong

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Object recognition has long been a challenging task in computer vision. Yet the human brain, with the ability to rapidly and accurately recognize visual stimuli, manages this task effortlessly. In the past decades, advances in neuroscience have revealed some neural mechanisms underlying visual processing. In this paper, we present a novel model inspired by the visual pathway in primate brains. This multi-layer neural network model imitates the hierarchical convergent processing mechanism in the visual pathway. We show that local image features generated by this model exhibit robust discrimination and even better generalization ability compared with some existing image descriptors. We also demonstrate the application of this model in an object recognition task on image data sets. The result provides strong support for the potential of this model.

Keywords: biological model, feature extraction, multi-layer neural network, object recognition

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Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

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Authors: Ilham Priadythama, Pringgo Widyo Laksono, Agung Pamungkas

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Myoelectric is prosthetic, flexible, and offered industrial application has been highly developed and widely used. Myoelectric hand use myoelectric signal from muscle to activate and control the membrane part of hand. Commonly myoelectric signal is detected on human arm from skin surface. So that it only small magnitude signal captured. Detecting myoelectric signal on the skin surface takes proper and consistent procedure. This paper provides preliminary study of electrodes position which gives best signal strength for five basic grasping. Two-position scenario used to place three channel electrodes set. A bi-potential amplifier based on AD620 used to amplify the signal. Finally, the signal was analyzed using DSSF3 software. From this study, we found that grasp type was stronger using first scenario electrode placement while the rest type better with another scenario.

Keywords: myoelectric signal, basic grasp, DSSF3, electrode, bi-potential amplifier

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Authors: Celso L. Fernando, Toshio Yoshii, Takahiro Tsubota

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Understanding the causes of a road accident and predicting their occurrence is key to preventing deaths and serious injuries from road accident events. Traditional statistical methods such as the Poisson and the Logistics regressions have been used to find the association of the traffic environmental factors with the accident occurred; recently, an artificial neural network, ANN, a computational technique that learns from historical data to make a more accurate prediction, has emerged. Although the ability to make accurate predictions, the ANN has difficulty dealing with highly unbalanced attribute patterns distribution in the training dataset; in such circumstances, the ANN treats the minority group as noise. However, in the real world data, the minority group is often the group of interest; e.g., in the road traffic accident data, the events of the accident are the group of interest. This study proposes a combination of the k-means with the ANN to improve the predictive ability of the neural network model by alleviating the effect of the unbalanced distribution of the attribute patterns in the training dataset. The results show that the proposed method improves the ability of the neural network to make a prediction on a highly unbalanced distributed attribute patterns dataset; however, on an even distributed attribute patterns dataset, the proposed method performs almost like a standard neural network.

Keywords: accident risks estimation, artificial neural network, deep learning, k-mean, road safety

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Authors: M. Khouil, N. Saber, M. Mestari

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In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: collision identification, fixed time, convex polyhedra, neural network, AMAXNET

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

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Authors: Les Moncrieff

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In a world awash with potent opioids flaming an international crisis, the need to explore safe alternatives has never been more urgent. Bio-electrode Therapy is a novel adjunctive treatment method for relieving acute opioid withdrawal symptoms and many types of complex acute and chronic pain (often the underlying cause of opioid dependence). By combining the science of developmental bioelectricity with Traditional Chinese Medicine’s theory of meridians, rapid relief from pain is routinely being achieved in the clinical setting. Human body functions are dependent on electrical factors, and acupuncture points on the body are known to have higher electrical conductivity than surrounding skin tissue. When tiny gold- and silver-plated electrodes are secured to the skin at specific acupuncture points using established Chinese Medicine principles and protocols, an enhanced microcurrent and electrical field are created between the electrodes, influencing the entire meridian and connecting meridians. No external power source or electrical devices are required. Endogenous DC electric fields are an essential fundamental component for development, regeneration, and wound healing. Disruptions in the normal ion-charge in the meridians and circulation of blood will manifest as pain and development of disease. With the application of these simple electrodes (gold acting as cathode and silver as anode) according to protocols, the resulting microcurrent is directed along the selected meridians to target injured or diseased organs and tissues. When injured or diseased cells have been stimulated by the microcurrent and electrical fields, the permeability of the cell membrane is affected, resulting in an immediate relief of pain, a rapid balancing of positive and negative ions (sodium, potassium, etc.) in the cells, the restoration of intracellular fluid levels, replenishment of electrolyte levels, pH balance, removal of toxins, and a re-establishment of homeostasis.

Keywords: bioelectricity, electrodes, electrical fields, acupuncture meridians, complex pain, opioid withdrawal management

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

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Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

Procedia PDF Downloads 136

Authors: Kais Manai

Abstract:

The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%.

Keywords: nuclear emulsion, particle identification, tracking, neural network

Procedia PDF Downloads 508

Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

Abstract:

Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

Procedia PDF Downloads 360