Search results for: multi-layer%20thin%20film
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 193

Search results for: multi-layer%20thin%20film

73 Buckling a Reservoir Composite Provided with Notches

Authors: H. Chenine, D. Ouinas, Z. Bennaceur

Abstract:

The thin shell structures like metal are particularly susceptible to buckling or geometric instability. Their sizing is performed by resorting to simplified rules, this approach is generally conservative. Indeed, these structures are very sensitive to the slightest imperfection shape (initial geometrical defects). The design is usually based on the knowledge of the real or perceived initial state. Now this configuration evolves over time, there is usually the addition of new deformities due to operation (accidental loads, creep), but also to loss of material located in the corroded areas. Taking into account these various damage generally led to a loss of bearing capacity. In order to preserve the charge potential of the structure, it is then necessary to find a different material. In our study we plan to replace the material used for reservoirs found in the company Sonatrach with a composite material made from carbon fiber or glass. 6 to 12 layers of composite are simply stuck. Research is devoted to the study of the buckling of multilayer shells subjected to an imposed displacement, allowed us to identify the key parameters and those whose effect is less. For all results, we find that the carbon epoxy T700E is the strongest, increasing the number of layers increases the strength of the shell.

Keywords: Finite Element Analysis, circular notches, buckling, tank made composite materials

Procedia PDF Downloads 329
72 Application of Data Mining Techniques for Tourism Knowledge Discovery

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

Procedia PDF Downloads 258
71 Optimization of Assay Parameters of L-Glutaminase from Bacillus cereus MTCC1305 Using Artificial Neural Network

Authors: P. Singh, R. M. Banik

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Artificial neural network (ANN) was employed to optimize assay parameters viz., time, temperature, pH of reaction mixture, enzyme volume and substrate concentration of L-glutaminase from Bacillus cereus MTCC 1305. ANN model showed high value of coefficient of determination (0.9999), low value of root mean square error (0.6697) and low value of absolute average deviation. A multilayer perceptron neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model and its topology was obtained as 5-3-1 after applying Levenberg Marquardt (LM) training algorithm. The predicted activity of L-glutaminase was obtained as 633.7349 U/l by considering optimum assay parameters, viz., pH of reaction mixture (7.5), reaction time (20 minutes), incubation temperature (35˚C), substrate concentration (40mM), and enzyme volume (0.5ml). The predicted data was verified by running experiment at simulated optimum assay condition and activity was obtained as 634.00 U/l. The application of ANN model for optimization of assay conditions improved the activity of L-glutaminase by 1.499 fold.

Keywords: Bacillus cereus, L-glutaminase, assay parameters, artificial neural network

Procedia PDF Downloads 388
70 Improving Forecasting Demand for Maintenance Spare Parts: Case Study

Authors: Abdulaziz Afandi

Abstract:

Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: neural network, LSTM, MLP, forecasting demand, inventory management

Procedia PDF Downloads 85
69 Device Control Using Brain Computer Interface

Authors: P. Neeraj, Anurag Sharma, Harsukhpreet Singh

Abstract:

In current years, Brain-Computer Interface (BCI) scheme based on steady-state Visual Evoked Potential (SSVEP) have earned much consideration. This study tries to evolve an SSVEP based BCI scheme that can regulate any gadget mock-up in two unique positions ON and OFF. In this paper, two distinctive gleam frequencies in low-frequency part were utilized to evoke the SSVEPs and were shown on a Liquid Crystal Display (LCD) screen utilizing Lab View. Two stimuli shading, Yellow, and Blue were utilized to prepare the system in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital part. Elements of the brain were separated by utilizing discrete wavelet Transform. A prominent system for multilayer system diverse Neural Network Algorithm (NNA), is utilized to characterize SSVEP signals. During training of the network with diverse calculation Regression plot results demonstrated that when Levenberg-Marquardt preparing calculation was utilized the exactness turns out to be 93.9%, which is superior to another training algorithm.

Keywords: brain computer interface, electroencephalography, steady-state visual evoked potential, wavelet transform, neural network

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68 Design and Thermal Simulation Analysis of the Chinese Accelerator Driven Sub-Critical System Injector-I Cryomodule

Authors: Rui-Xiong Han, Rui Ge, Shao-Peng Li, Lin Bian, Liang-Rui Sun, Min-Jing Sang, Rui Ye, Ya-Ping Liu, Xiang-Zhen Zhang, Jie-Hao Zhang, Zhuo Zhang, Jian-Qing Zhang, Miao-Fu Xu

Abstract:

The Chinese Accelerator Driven Sub-critical system (C-ADS) uses a high-energy proton beam to bombard the metal target and generate neutrons to deal with the nuclear waste. The Chinese ADS proton linear has two 0~10 MeV injectors and one 10~1500 MeV superconducting linac. Injector-I is studied by the Institute of High Energy Physics (IHEP) under construction in the Beijing, China. The linear accelerator consists of two accelerating cryomodules operating at the temperature of 2 Kelvin. This paper describes the structure and thermal performances analysis of the cryomodule. The analysis takes into account all the main contributors (support posts, multilayer insulation, current leads, power couplers, and cavities) to the static and dynamic heat load at various cryogenic temperature levels. The thermal simulation analysis of the cryomodule is important theory foundation of optimization and commissioning.

Keywords: C-ADS, cryomodule, structure, thermal simulation, static heat load, dynamic heat load

Procedia PDF Downloads 357
67 Response of Pavement under Temperature and Vehicle Coupled Loading

Authors: Yang Zhong, Mei-Jie Xu

Abstract:

To study the dynamic mechanics response of asphalt pavement under the temperature load and vehicle loading, asphalt pavement was regarded as multilayered elastic half-space system, and theory analysis was conducted by regarding dynamic modulus of asphalt mixture as the parameter. Firstly, based on the dynamic modulus test of asphalt mixture, function relationship between the dynamic modulus of representative asphalt mixture and temperature was obtained. In addition, the analytical solution for thermal stress in the single layer was derived by using Laplace integral transformation and Hankel integral transformation respectively by using thermal equations of equilibrium. The analytical solution of calculation model of thermal stress in asphalt pavement was derived by transfer matrix of thermal stress in multilayer elastic system. Finally, the variation of thermal stress in pavement structure was analyzed. The result shows that there is an obvious difference between the thermal stress based on dynamic modulus and the solution based on static modulus. Therefore, the dynamic change of parameter in asphalt mixture should be taken into consideration when the theoretical analysis is taken out.

Keywords: asphalt pavement, dynamic modulus, integral transformation, transfer matrix, thermal stress

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66 Analytical Modelling of Surface Roughness during Compacted Graphite Iron Milling Using Ceramic Inserts

Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

Procedia PDF Downloads 409
65 Applying Neural Networks for Solving Record Linkage Problem via Fuzzy Description Logics

Authors: Mikheil Kalmakhelidze

Abstract:

Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

Procedia PDF Downloads 234
64 Mesoporous Nanocomposites for Sustained Release Applications

Authors: Daniela Istrati, Alina Morosan, Maria Stanca, Bogdan Purcareanu, Adrian Fudulu, Laura Olariu, Alice Buteica, Ion Mindrila, Rodica Cristescu, Dan Eduard Mihaiescu

Abstract:

Our present work is related to the synthesis, characterization and applications of new nanocomposite materials based on silica mesoporous nanocompozites systems. The nanocomposite support was obtained by using a specific step–by–step multilayer structure buildup synthetic route, characterized by XRD (X-Ray Difraction), TEM (Transmission Electron Microscopy), FT-IR (Fourier Transform-Infra Red Spectrometry), BET (Brunauer–Emmett–Teller method) and loaded with Salvia officinalis plant extract obtained by a hydro-alcoholic extraction route. The sustained release of the target compounds was studied by a modified LC method, proving low release profiles, as expected for the high specific surface area support. The obtained results were further correlated with the in vitro / in vivo behavior of the nanocomposite material and recommending the silica mesoporous nanocomposites as good candidates for biomedical applications. Acknowledgements: This study has been funded by the Research Project PN-III-P2-2.1-PTE-2016-0160, 49-PTE / 2016 (PROZECHIMED) and Project Number PN-III-P4-ID-PCE-2016-0884 / 2017.

Keywords: biomedical, mesoporous, nanocomposites, natural products, sustained release

Procedia PDF Downloads 176
63 Experimental Investigation on Residual Stresses in Welded Medium-Walled I-shaped Sections Fabricated from Q460GJ Structural Steel Plates

Authors: Qian Zhu, Shidong Nie, Bo Yang, Gang Xiong, Guoxin Dai

Abstract:

GJ steel is a new type of high-performance structural steel which has been increasingly adopted in practical engineering. Q460GJ structural steel has a nominal yield strength of 460 MPa, which does not decrease significantly with the increase of steel plate thickness like normal structural steel. Thus, Q460GJ structural steel is normally used in medium-walled welded sections. However, research works on the residual stress in GJ steel members are few though it is one of the vital factors that can affect the member and structural behavior. This article aims to investigate the residual stresses in welded I-shaped sections fabricated from Q460GJ structural steel plates by experimental tests. A total of four full scale welded medium-walled I-shaped sections were tested by sectioning method. Both circular curve correction method and straightening measurement method were adopted in this study to obtain the final magnitude and distribution of the longitudinal residual stresses. In addition, this paper also explores the interaction between flanges and webs. And based on the statistical evaluation of the experimental data, a multilayer residual stress model is proposed.

Keywords: Q460GJ structural steel, residual stresses, sectioning method, welded medium-walled I-shaped sections

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62 Detection and Classification of Myocardial Infarction Using New Extracted Features from Standard 12-Lead ECG Signals

Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

Procedia PDF Downloads 418
61 Application of Deep Neural Networks to Assess Corporate Credit Rating

Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

Procedia PDF Downloads 199
60 Identification System for Grading Banana in Food Processing Industry

Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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59 Solar Radiation Time Series Prediction

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

Abstract:

A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

Procedia PDF Downloads 336
58 Analysis of Linguistic Disfluencies in Bilingual Children’s Discourse

Authors: Sheena Christabel Pravin, M. Palanivelan

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Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: bi-lingual, children who stutter, children with language impairment, hidden markov models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies

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57 Evaluation of High Temperature Wear Performance of as Cladded and Tig Re-Melting Stellite 6 Cladded Overlay on Aisi-304L Using SMAW Process

Authors: Manjit Singha, Sandeep Singh Sandhu, A. S. Shahi

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Stellite 6 is cobalt based superalloy used for protective coatings. It is used to improve the wear performance of stainless steel engineering components subjected to harsh environmental conditions. This paper reports the high temperature wear analysis of satellite 6 cladded on AISI 304 L substrate using SMAW process. Bead on plate experiment was carried out by varying current and electrode manipulation techniques to optimize the dilution and hardness. 80 Amp current and weaving technique was found to be the optimum set of parameters for overlaying which were further used for multipass multilayer cladding on two plates of AISI 304 L substrate. On the first plate, seven layers seven passes of stellite 6 was overlaid which was used in as cladded form and the second plate was overlaid with five layers five passes of satellite 6 with further TIG remelting. The wear performance was examined for normal temperature environmental condition and harsh temperature environmental condition. The satellite 6 coating with TIG remelting was found to be better in both the conditions even with lesser metal deposition due to its finer grain structure.

Keywords: surfacing, stellite 6, dilution, overlay, SMAW, high-temperature frictional wear, micro-structure, micro-hardness

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56 A Neural Network Classifier for Estimation of the Degree of Infestation by Late Blight on Tomato Leaves

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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55 Nelder-Mead Parametric Optimization of Elastic Metamaterials with Artificial Neural Network Surrogate Model

Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

Abstract:

Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

Procedia PDF Downloads 95
54 Hand Gesture Interpretation Using Sensing Glove Integrated with Machine Learning Algorithms

Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

Procedia PDF Downloads 252
53 To Ensure Maximum Voter Privacy in E-Voting Using Blockchain, Convolutional Neural Network, and Quantum Key Distribution

Authors: Bhaumik Tyagi, Mandeep Kaur, Kanika Singla

Abstract:

The advancement of blockchain has facilitated scholars to remodel e-voting systems for future generations. Server-side attacks like SQL injection attacks and DOS attacks are the most common attacks nowadays, where malicious codes are injected into the system through user input fields by illicit users, which leads to data leakage in the worst scenarios. Besides, quantum attacks are also there which manipulate the transactional data. In order to deal with all the above-mentioned attacks, integration of blockchain, convolutional neural network (CNN), and Quantum Key Distribution is done in this very research. The utilization of blockchain technology in e-voting applications is not a novel concept. But privacy and security issues are still there in a public and private blockchains. To solve this, the use of a hybrid blockchain is done in this research. This research proposed cryptographic signatures and blockchain algorithms to validate the origin and integrity of the votes. The convolutional neural network (CNN), a normalized version of the multilayer perceptron, is also applied in the system to analyze visual descriptions upon registration in a direction to enhance the privacy of voters and the e-voting system. Quantum Key Distribution is being implemented in order to secure a blockchain-based e-voting system from quantum attacks using quantum algorithms. Implementation of e-voting blockchain D-app and providing a proposed solution for the privacy of voters in e-voting using Blockchain, CNN, and Quantum Key Distribution is done.

Keywords: hybrid blockchain, secure e-voting system, convolutional neural networks, quantum key distribution, one-time pad

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52 Impact of Modifying the Surface Materials on the Radiative Heat Transfer Phenomenon

Authors: Arkadiusz Urzędowski, Dorota Wójcicka-Migasiuk, Andrzej Sachajdak, Magdalena Paśnikowska-Łukaszuk

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Due to the impact of climate changes and inevitability to reduce greenhouse gases, the need to use low-carbon and sustainable construction has increased. In this work, it is investigated how texture of the surface building materials and radiative heat transfer phenomenon in flat multilayer can be correlated. Attempts to test the surface emissivity are taken however, the trustworthiness of measurement results remains a concern since sensor size and thickness are common problems. This paper presents an experimental method to studies surface emissivity with use self constructed thermal sensors and thermal imaging technique. The surface of building materials was modified by mechanical and chemical treatment affecting the reduction of the emissivity. For testing the shaping surface of materials and mapping its three-dimensional structure, scanning profilometry were used in a laboratory. By comparing the results of laboratory tests and performed analysis of 3D computer fluid dynamics software, it can be shown that a change in the surface coverage of materials affects the heat transport by radiation between layers. Motivated by recent advancements in variational inference, this publication evaluates the potential use a dedicated data processing approach, and properly constructed temperature sensors, the influence of the surface emissivity on the phenomenon of radiation and heat transport in the entire partition can be determined.

Keywords: heat transfer, surface roughness, surface emissivity, radiation

Procedia PDF Downloads 51
51 Deep Reinforcement Learning Approach for Optimal Control of Industrial Smart Grids

Authors: Niklas Panten, Eberhard Abele

Abstract:

This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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50 Design of a Cooperative Neural Network, Particle Swarm Optimization (PSO) and Fuzzy Based Tracking Control for a Tilt Rotor Unmanned Aerial Vehicle

Authors: Mostafa Mjahed

Abstract:

Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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49 From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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48 Fabrication of Fe3O4core-meso SiO2/TiO2 Double Shell for Dye Pollution Remediation

Authors: Mohamed Habila, Ahmed Mohamed El-Toni, Mohamed Sheikh Moshab, Abdulrhman Al-Awadi, Zeid AL Othman

Abstract:

Water pollution with dyes is a critical environmental issue because off the huge amount of dyes disbarred annually, which cause severe damage for the ecosystem and human life. The main raison for this severs pollution is the rapid industrial development which led to more production of harmful pollutants. on the other hand, the core shell based magnetic materials have showed amazing character for controlling the material synthesis with the targeted structure to enhance the adsorptive removal of pollutants. Herein, the Fe3O4core-meso SiO2/TiO2 double shell have been prepared for methylene blue dye adsorption. the preparation procedure is controlled to prepare the magnetic core with further coating layers from silica and titania. The prepared Fe3O4core-meso SiO2/TiO2 double shell showed adsorption capacity for methylene blue removal about 50 mg/g at pH 6 after 80 min contact time form 50 ppm methylene blue solution. The adsorption process of methylene blue onto Fe3O4core-meso SiO2/TiO2 double shell was well fitted with the pseudo-second-order kinetic model and freundlish isotherm, indicating a quick and multilayer adsorption mechanism.

Keywords: magnetic core, silica shell, titania shell, water treatment, methylene blue, solvo-thermal process, adsorption

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47 Statistical Time-Series and Neural Architecture of Malaria Patients Records in Lagos, Nigeria

Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

Abstract:

Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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46 Testing Ammonia Borane for Multilayer Aprons in Nuclear Medicine as a Promising Non-toxic, Lightweight, Hydrogen Rich Material and to Enhance the Efficiency of Aprons for Workers Who Deal with Neutrons Radiation in Nuclear Medicine

Authors: Wed Othman Alghamdi

Abstract:

The current study aims to find a non-toxic, low density, hydrogen-rich material that can be used in aprons without causing health issues for nuclear medical workers that could hinder their work and negatively affect patients. Five samples were tested in terms of fast neutron removal cross-section(C21H25ClO5, C2H4, LiH,H3NBH3,MgH2) mathematically using computer program called Phy-x/PSD it is a computer program designed to calculate the fast neutron removal cross section, and it was obtained that ammonia borane (𝐻3𝑁𝐵𝐻3) with a density of 0.78 (g/ cm3) ,And it containment of the three most important elements that play a major role in protection shields, which are (hydrogen, boron, nitrogen), Hydrogen works as a moderator that slows neutrons and turn them into thermal neutrons, boron and nitrogen both have the largest neutron absorption cross section. Ammonia borane has the highest fast neutron removal cross-section with the value of (0.122959317985393cm-1) and the least for polyethylene (𝐶2𝐻4) with the value of (0.0838038707225853 cm-1) which made the ammonia borane a better candidate than polyethylene and other compounds that have been tasted in previous research for multi-layer aprons in nuclear medicine, and may approve a proper protection against the hazard radiations that its produced in nuclear medicine filed by several ways, due to it is low density and non-toxicity.

Keywords: aprons, radiation, non-toxic, nuclear medicine, neutrons

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45 New Applications of Essential Oils: Edible Packaging Material for Food Supplements

Authors: Roxana Gheorghita, Gheorghe Gutt

Abstract:

Environmental pollution due to non-degradation of packaging from the food and pharmaceutical industry is reaching increasingly alarming levels. The packaging used for food supplements is usually composed of successive layers of synthetic materials, conventional, glue, and paint. The situation is becoming more and more problematic as the population, according to statistics, uses food supplements more and more often. The solution can be represented by edible packaging, completely biodegradable, and compostable. The tested materials were obtained from biopolymers, agar, carrageenan, and alginate, in well-established quantities and plasticized with glycerol. Rosemary, thyme, and oregano essential oils have been added in varying proportions. The obtained films are completely water-soluble in hot liquids (with a temperature of about 80° C) and can be consumed with the product contained. The films were glossy, pleasant to the touch, thin (thicknesses between 32.8 and 52.8 μm), transparent, and with a pleasant smell, specific to the added essential oil. Tested for microbial evaluation, none of the films indicated the presence of E. coli, S. aureus, enterobacteria, coliform bacteria, yeasts, or molds. This aspect can also be helped by the low values of the water activity index (located between 0.546 and 0.576). The mechanical properties indicated that the material became more resistant with the addition of essential oil, the best values being recorded by the addition of oregano. The results obtained indicate the possibility of using biopolymer-based films with the addition of rosemary, thyme, and oregano essential oil, for wrapping food supplements, thus replacing conventional packaging, multilayer, impossible to sort and recycle.

Keywords: edible films, food supplements, oregano, rosemary, thyme

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44 First-Principles Calculations of Hydrogen Adsorbed in Multi-Layer Graphene

Authors: Mohammad Shafiul Alam, Mineo Saito

Abstract:

Graphene-based materials have attracted much attention because they are candidates for post silicon materials. Since controlling of impurities is necessary to achieve nano device, we study hydrogen impurity in multi-layer graphene. We perform local spin Density approximation (LSDA) in which the plane wave basis set and pseudopotential are used. Previously hydrogen monomer and dimer in graphene is well theoretically studied. However, hydrogen on multilayer graphene is still not clear. By using first-principles electronic structure calculations based on the LSDA within the density functional theory method, we studied hydrogen monomers and dimers in two-layer graphene. We found that the monomers are spin-polarized and have magnetic moment 1 µB. We also found that most stable dimer is much more stable than monomer. In the most stable structures of the dimers in two-layer graphene, the two hydrogen atoms are bonded to the host carbon atoms which are nearest-neighbors. In this case two hydrogen atoms are located on the opposite sides. Whereas, when the two hydrogen atoms are bonded to the same sublattice of the host materials, magnetic moments of 2 µB appear in two-layer graphene. We found that when the two hydrogen atoms are bonded to third-nearest-neighbor carbon atoms, the electronic structure is nonmagnetic. We also studied hydrogen monomers and dimers in three-layer graphene. The result is same as that of two-layer graphene. These results are very important in the field of carbon nanomaterials as it is experimentally difficult to show the magnetic state of those materials.

Keywords: first-principles calculations, LSDA, multi-layer gra-phene, nanomaterials

Procedia PDF Downloads 291