Search results for: neural tube defects
2415 A Neural Network for the Prediction of Contraction after Burn Injuries
Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen
Abstract:
A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound
Procedia PDF Downloads 502414 Review of Hydrologic Applications of Conceptual Models for Precipitation-Runoff Process
Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade
Abstract:
The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model
Procedia PDF Downloads 4422413 The Estimation Method of Inter-Story Drift for Buildings Based on Evolutionary Learning
Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park
Abstract:
The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm
Procedia PDF Downloads 3232412 Compressive Strength Evaluation of Underwater Concrete Structures Integrating the Combination of Rebound Hardness and Ultrasonic Pulse Velocity Methods with Artificial Neural Networks
Authors: Seunghee Park, Junkyeong Kim, Eun-Seok Shin, Sang-Hun Han
Abstract:
In this study, two kinds of nondestructive evaluation (NDE) techniques (rebound hardness and ultrasonic pulse velocity methods) are investigated for the effective maintenance of underwater concrete structures. A new methodology to estimate the underwater concrete strengths more effectively, named “artificial neural network (ANN) – based concrete strength estimation with the combination of rebound hardness and ultrasonic pulse velocity methods” is proposed and verified throughout a series of experimental works.Keywords: underwater concrete, rebound hardness, Schmidt hammer, ultrasonic pulse velocity, ultrasonic sensor, artificial neural networks, ANN
Procedia PDF Downloads 5232411 An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models
Authors: Christopher Godwin Udomboso, Angela Unna Chukwu, Isaac Kwame Dontwi
Abstract:
In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC.Keywords: statistical neural network, network information criterion, adjusted network, information criterion, transfer function
Procedia PDF Downloads 5582410 Malignancy Assessment of Brain Tumors Using Convolutional Neural Network
Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh
Abstract:
The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging
Procedia PDF Downloads 1412409 Electric Load Forecasting Based on Artificial Neural Network for Iraqi Power System
Authors: Afaneen Anwer, Samara M. Kamil
Abstract:
Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load.Keywords: load forecasting, neural network, back-propagation algorithm, Iraqi power system
Procedia PDF Downloads 5722408 Artificial Neural Network in FIRST Robotics Team-Based Prediction System
Authors: Cedric Leong, Parth Desai, Parth Patel
Abstract:
The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)
Procedia PDF Downloads 5072407 Production and Characterization of Implant Material Produced by Using Electroless Ni Plated Al2O3-Co-Cr-Ti Powders
Authors: Ahmet Yonetken, Ayhan Erol
Abstract:
The microstructure, mechanical properties and corrosion characteristics of Ni plated %10Al2O3-%40Co-%20Cr and %10Ti powders were investigated using specimens produced by tube furnace sintering at 800-1200°C temperature. A uniform nickel layer on Al2O3-Co-Cr and Ti powders was deposited prior to sintering using electroless plating technique. A composite consisting of quintet additions, a metallic phase, Ti,Cr and Co including a ceramic phase, alumina, within a matrix of Ni has been prepared under Ar shroud and then tube furnace sintered. XRD, SEM (Scanning Electron Microscope), corrosion behavior in acidic media were investigated to characterize the properties of the specimens. Experimental results carried out for composition (%10Al2O3-%40Co-%20Cr- %10Ti)20Ni at 1200°C suggest that the best properties as 312.18HV were obtained at 1200°C.Keywords: sintering, intermetallic, Electroless nickel plating, composite
Procedia PDF Downloads 5702406 The Application of a Neural Network in the Reworking of Accu-Chek to Wrist Bands to Monitor Blood Glucose in the Human Body
Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi
Abstract:
The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.Keywords: Accu-Check, diabetes, neural network, pattern recognition
Procedia PDF Downloads 1412405 Multimodal Direct Neural Network Positron Emission Tomography Reconstruction
Authors: William Whiteley, Jens Gregor
Abstract:
In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography
Procedia PDF Downloads 1052404 Determination of the Botanical Origin of Honey by the Artificial Neural Network Processing of PARAFAC Scores of Fluorescence Data
Authors: Lea Lenhardt, Ivana Zeković, Tatjana Dramićanin, Miroslav D. Dramićanin
Abstract:
Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and artificial neural networks (ANN) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. Fluorescence spectra were described with a six-component PARAFAC model, and PARAFAC scores were further processed with two types of ANN’s (feed-forward network and self-organizing maps) to obtain algorithms for classification of honey on the basis of their botanical origin. Both ANN’s detected fake honey samples with 100% sensitivity and specificity.Keywords: honey, fluorescence, PARAFAC, artificial neural networks
Procedia PDF Downloads 9472403 Study on NOₓ Emission Characteristics of Internal Gas Recirculation Technique
Authors: DaeHae Kim, MinJun Kwon, Sewon Kim
Abstract:
This study is aimed to develop ultra-low NOₓ burner using the internal recirculation of flue gas inside the combustion chamber that utilizes the momentum of intake fuel and air. Detailed experimental investigations are carried out to study these fluid dynamic effects on the emission characteristics of newly developed burner in industrial steam boiler system. Experimental parameters are distance of Venturi tube from burner, Coanda nozzle gap distance, and air sleeve length at various fuel/air ratio and thermal heat load conditions. The results showed that NOₓ concentration decreases as the distance of Venturi tube from burner increases. The CO concentration values at all operating conditions were negligible. In addition, the increase of the Coanda nozzle gap distance decreased the NOₓ concentration. It is experimentally found out that both fuel injection recirculation and air injection recirculation technique was very effective in reducing NOₓ formation.Keywords: Coanda effect, combustion, burner, low NOₓ
Procedia PDF Downloads 1962402 Performance Analysis of Artificial Neural Network with Decision Tree in Prediction of Diabetes Mellitus
Authors: J. K. Alhassan, B. Attah, S. Misra
Abstract:
Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus
Procedia PDF Downloads 4042401 Estimation of Pressure Loss Coefficients in Combining Flows Using Artificial Neural Networks
Authors: Shahzad Yousaf, Imran Shafi
Abstract:
This paper presents a new method for calculation of pressure loss coefficients by use of the artificial neural network (ANN) in tee junctions. Geometry and flow parameters are feed into ANN as the inputs for purpose of training the network. Efficacy of the network is demonstrated by comparison of the experimental and ANN based calculated data of pressure loss coefficients for combining flows in a tee junction. Reynolds numbers ranging from 200 to 14000 and discharge ratios varying from minimum to maximum flow for calculation of pressure loss coefficients have been used. Pressure loss coefficients calculated using ANN are compared to the models from literature used in junction flows. The results achieved after the application of ANN agrees reasonably to the experimental values.Keywords: artificial neural networks, combining flow, pressure loss coefficients, solar collector tee junctions
Procedia PDF Downloads 3822400 Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks
Authors: L. Parisi
Abstract:
Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation
Procedia PDF Downloads 4372399 Improving the Crashworthiness Characteristics of Long Steel Circular Tubes Subjected to Axial Compression by Inserting a Helical Spring
Authors: Mehdi Tajdari, Farzad Mokhtarnejad, Fatemeh Moradi, Mehdi Najafizadeh
Abstract:
Nowadays, energy absorbing devices have been widely used in all vehicles and moving parts such as railway couches, aircraft, ships and lifts. The aim is to protect these structures from serious damages while subjected to impact loads, or to minimize human injuries while collision is occurred in transportation systems. These energy-absorbing devices can dissipate kinetic energy in a wide variety of ways like friction, facture, plastic bending, crushing, cyclic plastic deformation and metal cutting. On the other hand, various structures may be used as collapsible energy absorbers. Metallic cylindrical tubes have attracted much more attention due to their high stiffness and strength combined with the low weight and ease of manufacturing process. As a matter of fact, favorable crash worthiness characteristics for energy dissipation purposes can be achieved from axial collapse of tubes while they crush progressively in symmetric modes. However, experimental and theoretical results have shown that depending on various parameters such as tube geometry, material properties of tube, boundary and loading conditions, circular tubes buckle in different modes of deformation, namely, diamond and Euler collapsing modes. It is shown that when the tube length is greater than the critical length, the tube deforms in overall Euler buckling mode, which is an inefficient mode of energy absorption and needs to be avoided in crash worthiness applications. This study develops a new method with the aim of improving energy absorption characteristics of long steel circular tubes. Inserting a helical spring into the tubes is proved experimentally to be an efficient solution. In fact when a long tube is subjected to axial compression load, the spring prevents of undesirable Euler or diamond collapsing modes. This is because the spring reinforces the internal wall of tubes and it causes symmetric deformation in tubes. In this research three specimens were prepared and three tests were performed. The dimensions of tubes were selected so that in axial compression load buckling is occurred. In the second and third tests a spring was inserted into tubes and they were subjected to axial compression load in quasi-static and impact loading, respectively. The results showed that in the second and third tests buckling were not happened and the tubes deformed in symmetric modes which are desirable in energy absorption.Keywords: energy absorption, circular tubes, collapsing deformation, crashworthiness
Procedia PDF Downloads 3352398 Artificial Neural Network-Based Bridge Weigh-In-Motion Technique Considering Environmental Conditions
Authors: Changgil Lee, Junkyeong Kim, Jihwan Park, Seunghee Park
Abstract:
In this study, bridge weigh-in-motion (BWIM) system was simulated under various environmental conditions such as temperature, humidity, wind and so on to improve the performance of the BWIM system. The environmental conditions can make difficult to analyze measured data and hence those factors should be compensated. Various conditions were considered as input parameters for ANN (Artificial Neural Network). The number of hidden layers for ANN was decided so that nonlinearity could be sufficiently reflected in the BWIM results. The weight of vehicles and axle weight were more accurately estimated by applying ANN approach. Additionally, the type of bridge which was a target structure was considered as an input parameter for the ANN.Keywords: bridge weigh-in-motion (BWIM) system, environmental conditions, artificial neural network, type of bridges
Procedia PDF Downloads 4322397 Experimental Study on Capturing of Magnetic Nanoparticles Transported in an Implant Assisted Cylindrical Tube under Magnetic Field
Authors: Anurag Gaur Nidhi
Abstract:
Targeted drug delivery is a method of delivering medication to a patient in a manner that increases the concentration of the medication in some parts of the body relative to others. Targeted drug delivery seeks to concentrate the medication in the tissues of interest while reducing the relative concentration of the medication in the remaining tissues. This improves efficacy of the while reducing side effects. In the present work, we investigate the effect of magnetic field, flow rate and particle concentration on the capturing of magnetic particles transported in a stent implanted fluidic channel. Iron oxide magnetic nanoparticles (Fe3O4) nanoparticles were synthesized via co-precipitation method. The synthesized Fe3O4 nanoparticles were added in the de-ionized (DI) water to prepare the Fe3O4 magnetic particle suspended fluid. This fluid is transported in a cylindrical tube of diameter 8 mm with help of a peristaltic pump at different flow rate (25-40 ml/min). A ferromagnetic coil of SS 430 has been implanted inside the cylindrical tube to enhance the capturing of magnetic nanoparticles under magnetic field. The capturing of magnetic nanoparticles was observed at different magnetic magnetic field, flow rate and particle concentration. It is observed that capture efficiency increases from 47-67 % at magnetic field 2-5kG, respectively at particle concentration 0.6 mg/ml and at flow rate 30 ml/min. However, the capture efficiency decreases from 65 to 44 % by increasing the flow rate from 25 to 40 ml/min, respectively. Furthermore, it is observed that capture efficiency increases from 51 to 67 % by increasing the particle concentration from 0.3 to 0.6 mg/ml, respectively.Keywords: capture efficiency, implant assisted-Magnetic drug targeting (IA-MDT), magnetic nanoparticles, In-vitro study
Procedia PDF Downloads 2972396 Neural Network Analysis Applied to Risk Prediction of Early Neonatal Death
Authors: Amanda R. R. Oliveira, Caio F. F. C. Cunha, Juan C. L. Junior, Amorim H. P. Junior
Abstract:
Children deaths are traumatic events that most often can be prevented. The technology of prevention and intervention in cases of infant deaths is available at low cost and with solid evidence and favorable results, however, with low access cover. Weight is one of the main factors related to death in the neonatal period, so the newborns of low birth weight are a population at high risk of death in the neonatal period, especially early neonatal period. This paper describes the development of a model based in neural network analysis to predict the mortality risk rating in the early neonatal period for newborns of low birth weight to identify the individuals of this population with increased risk of death. The neural network applied was trained with a set of newborns data obtained from Brazilian health system. The resulting network presented great success rate in identifying newborns with high chances of death, which demonstrates the potential for using this tool in an integrated manner to the health system, in order to direct specific actions for improving prognosis of newborns.Keywords: low birth weight, neonatal death risk, neural network, newborn
Procedia PDF Downloads 4422395 Aerobic Bioprocess Control Using Artificial Intelligence Techniques
Authors: M. Caramihai, Irina Severin
Abstract:
This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques
Procedia PDF Downloads 4102394 Comparative Connectionism: Study of the Biological Constraints of Learning Through the Manipulation of Various Architectures in a Neural Network Model under the Biological Principle of the Correlation Between Structure and Function
Authors: Giselle Maggie-Fer Castañeda Lozano
Abstract:
The main objective of this research was to explore the role of neural network architectures in simulating behavioral phenomena as a potential explanation for selective associations, specifically related to biological constraints on learning. Biological constraints on learning refer to the limitations observed in conditioning procedures, where learning is expected to occur. The study involved simulations of five different experiments exploring various phenomena and sources of biological constraints in learning. These simulations included the interaction between response and reinforcer, stimulus and reinforcer, specificity of stimulus-reinforcer associations, species differences, neuroanatomical constraints, and learning in uncontrolled conditions. The overall results demonstrated that by manipulating neural network architectures, conditions can be created to model and explain diverse biological constraints frequently reported in comparative psychology literature as learning typicities. Additionally, the simulations offer predictive content worthy of experimental testing in the pursuit of new discoveries regarding the specificity of learning. The implications and limitations of these findings are discussed. Finally, it is suggested that this research could inaugurate a line of inquiry involving the use of neural networks to study biological factors in behavior, fostering the development of more ethical and precise research practices.Keywords: comparative psychology, connectionism, conditioning, experimental analysis of behavior, neural networks
Procedia PDF Downloads 592393 Simulation of Heat Exchanger Behavior during LOCA Accident in THTL Test Loop
Authors: R. Mahmoodi, A. R. Zolfaghari
Abstract:
In nuclear power plants, loss of coolant from the primary system is the type of reduced removed capacity that is given most attention; such an accident is referred as Loss of Coolant Accident (LOCA). In the current study, investigation of shell and tube THTL heat exchanger behavior during LOCA is implemented by ANSYS CFX simulation software in both steady state and transient mode of turbulent fluid flow according to experimental conditions. Numerical results obtained from ANSYS CFX simulation show good agreement with experimental data of THTL heat exchanger. The results illustrate that in large break LOCA as short term accident, heat exchanger could not fast response to temperature variables but in the long term, the temperature of shell side of heat exchanger will be increase.Keywords: shell-and-tube heat exchanger, shell-side, CFD, flow and heat transfer, LOCA
Procedia PDF Downloads 4352392 Automating 2D CAD to 3D Model Generation Process: Wall pop-ups
Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski
Abstract:
In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection
Procedia PDF Downloads 1392391 Thick Data Analytics for Learning Cataract Severity: A Triplet Loss Siamese Neural Network Model
Authors: Jinan Fiaidhi, Sabah Mohammed
Abstract:
Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network.Keywords: thick data analytics, siamese neural network, triplet-loss model, few shot learning
Procedia PDF Downloads 1022390 Performance Analysis of Artificial Neural Network Based Land Cover Classification
Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul
Abstract:
Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5
Procedia PDF Downloads 5342389 Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks
Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang
Abstract:
For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network
Procedia PDF Downloads 4222388 Stereotypical Motor Movement Recognition Using Microsoft Kinect with Artificial Neural Network
Authors: M. Jazouli, S. Elhoufi, A. Majda, A. Zarghili, R. Aalouane
Abstract:
Autism spectrum disorder is a complex developmental disability. It is defined by a certain set of behaviors. Persons with Autism Spectrum Disorders (ASD) frequently engage in stereotyped and repetitive motor movements. The objective of this article is to propose a method to automatically detect this unusual behavior. Our study provides a clinical tool which facilitates for doctors the diagnosis of ASD. We focus on automatic identification of five repetitive gestures among autistic children in real time: body rocking, hand flapping, fingers flapping, hand on the face and hands behind back. In this paper, we present a gesture recognition system for children with autism, which consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using artificial neural network (ANN). The first one uses the Microsoft Kinect sensor, the second one chooses points of interest from the 3D skeleton to characterize the gestures, and the last one proposes a neural connectionist model to perform the supervised classification of data. The experimental results show that our system can achieve above 93.3% recognition rate.Keywords: ASD, artificial neural network, kinect, stereotypical motor movements
Procedia PDF Downloads 3012387 An Audit on Tracheal Tube Cuff Pressure Check and Monitoring during Current Practice
Authors: Mahmoud Hassanin, Roshan Thawale, Kiran Yelamati
Abstract:
Background: During current practice, intraoperative regular endotracheal cuff pressure monitoring is not routine, despite the significant number of clinicians interested in checking it after intubation to ensure a good seal and adequate ventilation. Aims and objectives: to highlight that the current practice has no guidance related to regular intra-operative monitoring of the endotracheal tube cuff pressure, which can improve patient safety and post-operative experience. Methods: local department survey was done targeting anaesthetists' current practice, measuring their knowledge and problem awareness to improve patient satisfaction and change the current approach. Results: The participants were not using the manometer, despite their interest in ensuring that the cuff pressure was high enough and there was a proper seal. More than 50% of the participant don't know the ideal range of the endotracheal tube cuff pressure range, and 32% don't know whether it is available or not in the theatre. Despite the previous finding, 100% of the participants used different methods to ensure adequate cuff pressure. The collected data revealed that at least 26% of the participant confirmed that they had seen patients having post-intubation complications. Conclusion: There is an increasing importance placed on quality assurance. Clinical practice varies widely among practitioners, with the only consistency being the omission of cuff manometers during routine intra-operative management, despite their proven benefit and efficacy. Encourage the anaesthetists and ODPs to use cuff pressure manometers. The availability of portable pressure manometers can help to maintain safe cuff pressures in patients requiring endotracheal intubation.Keywords: endotracheal cuff pressure, intra-operative monitoring, current practice, patient satisfaction
Procedia PDF Downloads 1022386 Efficient Neural and Fuzzy Models for the Identification of Dynamical Systems
Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe
Abstract:
The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.Keywords: non-linear systems, fuzzy set Models, neural network, control law
Procedia PDF Downloads 206