Search results for: back propagation neural network model.

Authors: Supachai Tangwongsan, Labhidhorn Pangphuthipong

Abstract:

This research work proposes a model of network security systems aiming to prevent production system in a data center from being attacked by intrusions. Conceptually, we introduce a decoy system as a part of the security system for luring intrusions, and apply network intrusion detection (NIDS), coupled with the decoy system to perform intrusion prevention. When NIDS detects an activity of intrusions, it will signal a redirection module to redirect all malicious traffics to attack the decoy system instead, and hence the production system is protected and safe. However, in a normal situation, traffic will be simply forwarded to the production system as usual. Furthermore, we assess the performance of the model with various bandwidths, packet sizes and inter-attack intervals (attacking frequencies).

Keywords: Intrusion detection, Decoy, Snort, Intrusion prevention.

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Authors: Taneli Eisto, Venlakaisa Hölttä, Katrine Mahlamäki, Janne Kollanus, Marko Nieminen

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Early supplier involvement (ESI) benefits new product development projects several ways. Nevertheless, many castuser companies do not know the advantages of ESI and therefore do not utilize it. This paper presents reasons why to utilize ESI in casting industry and how that can be done. Further, this paper presents advantages and challenges related to ESI in casting industry, and introduces a Casting-Network Collaboration Model. The model presents practices for companies to build advantageous collaborative relationships. More detailed, the model describes three levels for company-network relationships in casting industry with different degrees of collaboration, and requirements for operating in each level. In our research, ESI was found to influence, for example, on project time, component cost, and quality. In addition, challenges related to ESI, such as, a lack of mutual trust and unawareness about the advantages were found. Our research approach was a case study including four cases.

Keywords: Casting Industry, Collaboration Model, EarlySupplier Involvement, New Product Development.

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Authors: Hsin-Yun Lee, Hao-Hsi Tseng

Abstract:

Previous studies mass evacuation route network does not fully reflect the step-by-step behavior and evacuees make routing decisions. Therefore, they do not work as expected when applied to the evacuation route planning is valid. This article describes where evacuees may have to make a direction to select all areas were identified as guiding points to improve evacuation routes network. This improved route network can be used as a basis for the layout can be used to guide the signs indicate that provides the required evacuation direction. This article also describes that combines simulation and artificial bee colony algorithm to provide the proposed routing solutions, to plan an integrated routing mode. The improved network and the model used is the cinema as a case study to assess the floor. The effectiveness of guidance solution in the total evacuation time is significant by verification.

Keywords: Artificial bee colony, Evacuation, Simulation.

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Authors: Suryanto, Hyo-Kak Kim, Sang-Hee Park, Dae-Hwan Kim, Sung-Jea Ko

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In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.

Keywords: center voting, back projection, object tracking, size adaptation, non-stationary camera tracking.

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Authors: Anilkumar Kothalil Gopalakrishnan

Abstract:

A subjectively influenced router for vehicles in a fourjunction traffic system is presented. The router is based on a 3-layer Backpropagation Neural Network (BPNN) and a greedy routing procedure. The BPNN detects priorities of vehicles based on the subjective criteria. The subjective criteria and the routing procedure depend on the routing plan towards vehicles depending on the user. The routing procedure selects vehicles from their junctions based on their priorities and route them concurrently to the traffic system. That is, when the router is provided with a desired vehicles selection criteria and routing procedure, it routes vehicles with a reasonable junction clearing time. The cost evaluation of the router determines its efficiency. In the case of a routing conflict, the router will route the vehicles in a consecutive order and quarantine faulty vehicles. The simulations presented indicate that the presented approach is an effective strategy of structuring a subjective vehicle router.

Keywords: Backpropagation Neural Network, Backpropagationalgorithm, Greedy routing procedure, Subjective criteria, Vehiclepriority, Cost evaluation, Route generation

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Authors: Ali Rafiee, Ahad Salimi, Ali Reza Roosta

Abstract:

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound (TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a novel method for automatic prostate segmentation in TRUS images is presented. This method involves preprocessing (edge preserving noise reduction and smoothing) and prostate segmentation. The speckle reduction has been achieved by using stick filter and top-hat transform has been implemented for smoothing. A feed forward neural network and local binary pattern together have been use to find a point inside prostate object. Finally the boundary of prostate is extracted by the inside point and an active contour algorithm. A numbers of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with MSE less than 4.6% relative to boundary provided manually by physicians.

Keywords: Prostate segmentation, stick filter, neural network, active contour.

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma

Abstract:

All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.

Keywords: MIMO, Artificial Neural Network (ANN), CMA, LS, CSI.

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Authors: N. R. Harris, J. Curry

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This paper describes the construction and operation of an experimental LoRaWAN network surrounding the University of Southampton in the United Kingdom. Following successful installation, an experimental node design is built and characterised, with particular emphasis on radio range. Several configurations are investigated, including different data rates, and varying heights of node. It is concluded that although range can be great (over 8 km in this case), environmental topology is critical. However, shorter range implementations, up to about 2 km in an urban environment, are relatively insensitive although care is still needed. The example node and the relatively simple base station reported demonstrate that LoraWan can be a very low cost and practical solution to Internet of Things type applications for distributed monitoring systems with sensors spread over distances of several km.

Keywords: Wireless sensor network, LoRa, internet of things, propagation.

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Authors: M. Sedighizadeh, A. Rezazadeh

Abstract:

Movable power sources of proton exchange membrane fuel cells (PEMFC) are the important research done in the current fuel cells (FC) field. The PEMFC system control influences the cell performance greatly and it is a control system for industrial complex problems, due to the imprecision, uncertainty and partial truth and intrinsic nonlinear characteristics of PEMFCs. In this paper an adaptive PI control strategy using neural network adaptive Morlet wavelet for control is proposed. It is based on a single layer feed forward neural networks with hidden nodes of adaptive morlet wavelet functions controller and an infinite impulse response (IIR) recurrent structure. The IIR is combined by cascading to the network to provide double local structure resulting in improving speed of learning. The proposed method is applied to a typical 1 KW PEMFC system and the results show the proposed method has more accuracy against to MLP (Multi Layer Perceptron) method.

Keywords: Adaptive Control, Morlet Wavelets, PEMFC.

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: Dialogue management, response generation, reinforcement learning, deep learning, evaluation.

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Authors: D. Prangchumpol

Abstract:

This paper is a description approach to predict incoming and outgoing data rate in network system by using association rule discover, which is one of the data mining techniques. Information of incoming and outgoing data in each times and network bandwidth are network performance parameters, which needed to solve in the traffic problem. Since congestion and data loss are important network problems. The result of this technique can predicted future network traffic. In addition, this research is useful for network routing selection and network performance improvement.

Keywords: Traffic prediction, association rule, data mining.

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient, but not the magnitude. A neural network with two hidden layers was then used to learn the coefficient magnitudes, along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: Quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction.

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Authors: Daniel Rivero, Julián Dorado, Juan R. Rabuñal, Alejandro Pazos, Javier Pereira

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The development of Artificial Neural Networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques.

Keywords: Artificial Neural Networks, Evolutionary Computation, Genetic Programming.

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: Malaria, deep learning, DL, convolution neural network, CNN, thin blood smears.

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Authors: A. Chellil, A. Nour, S. Lecheb, H. Mechakra, L. Addar, H. Kebir

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This paper present a mechanical characteristics on fatigue crack propagation in Aluminium Plate based on strain and stress distribution using the abaqus software. The changes in shear strain and stress distribution during the fatigue cycle with crack growth is identified. In progressive crack in the strain distribution and the stress is increase in the critical zone. Numerical Modal analysis of the model developed, prove that the Eigen frequencies of aluminium plate were decreased after cracking, and this reduce is nonlinear. These results can provide a reference for analysts and designers of aluminium alloys in aeronautical systems.

Therefore, the modal analysis is an important factor for monitoring the aeronautic structures.

Keywords: Aluminium alloys, plate, crack, failure.

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Authors: Kenichiro Hida, Shin-Ichi Kuribayashi

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In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.

Keywords: Virtual routing function, NFV, resource allocation, minimum power consumption.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

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The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets, especially in the motorist sector, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of Python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. 60 vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes that the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: Convolutional Neural Network, CNN, location identification, tracking, GPS, GSM.

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Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

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Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may damage ToM by affecting on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks, (1) each patient group performed worse than HC; (2) there were no significant differences between LTLE and RTLE groups; and (3) the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: Cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind.

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: Pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction.

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Authors: H. Shareef, A. Mohamed, S. A. Khalid, Aziah Khamis

Abstract:

This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation. 

Keywords: Artificial intelligence, Power tracing, Artificial neural network, ANFIS, Power system deregulation.

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Authors: Satyendra Nath Mandal, J. Pal Choudhury, Dilip De, S. R. Bhadra Chaudhuri

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The production of a plant can be measured in terms of seeds. The generation of seeds plays a critical role in our social and daily life. The fruit production which generates seeds, depends on the various parameters of the plant, such as shoot length, leaf number, root length, root number, etc When the plant is growing, some leaves may be lost and some new leaves may appear. It is very difficult to use the number of leaves of the tree to calculate the growth of the plant.. It is also cumbersome to measure the number of roots and length of growth of root in several time instances continuously after certain initial period of time, because roots grow deeper and deeper under ground in course of time. On the contrary, the shoot length of the tree grows in course of time which can be measured in different time instances. So the growth of the plant can be measured using the data of shoot length which are measured at different time instances after plantation. The environmental parameters like temperature, rain fall, humidity and pollution are also play some role in production of yield. The soil, crop and distance management are taken care to produce maximum amount of yields of plant. The data of the growth of shoot length of some mustard plant at the initial stage (7,14,21 & 28 days after plantation) is available from the statistical survey by a group of scientists under the supervision of Prof. Dilip De. In this paper, initial shoot length of Ken( one type of mustard plant) has been used as an initial data. The statistical models, the methods of fuzzy logic and neural network have been tested on this mustard plant and based on error analysis (calculation of average error) that model with minimum error has been selected and can be used for the assessment of shoot length at maturity. Finally, all these methods have been tested with other type of mustard plants and the particular soft computing model with the minimum error of all types has been selected for calculating the predicted data of growth of shoot length. The shoot length at the stage of maturity of all types of mustard plants has been calculated using the statistical method on the predicted data of shoot length.

Keywords: Fuzzy time series, neural network, forecasting error, average error.

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Authors: A. Bumbová, J. Kellner, J. Navrátil, D. Pluskal, M. Kozubková, E. Kozubek

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The paper deals with the possibilities of modelling vapour propagation of explosive substances in the FLUENT software. With regard to very low tensions of explosive substance vapours the experiment has been verified as exemplified by mononitrotoluene. Either constant or time variable meteorological conditions have been used for calculation. Further, it has been verified that the eluent source may be time-dependent and may reflect a real situation or the liberation rate may be constant. The execution of the experiment as well as evaluation were clear and it could also be used for modelling vapour and aerosol propagation of selected explosive substances in the atmospheric boundary layer.

Keywords: atmospheric boundary layer, explosive substances, FLUENT software, modelling of propagation

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Authors: A. M. El-Zonkoly

Abstract:

In this paper, based on steady-state models of Flexible AC Transmission System (FACTS) devices, the sizing of static synchronous series compensator (SSSC) controllers in transmission network is formed as an optimization problem. The objective of this problem is to reduce the transmission losses in the network. The optimization problem is solved using particle swarm optimization (PSO) technique. The Newton-Raphson load flow algorithm is modified to consider the insertion of the SSSC devices in the network. A numerical example, illustrating the effectiveness of the proposed algorithm, is introduced. In addition, a novel model of a 3- phase voltage source converter (VSC) that is suitable for series connected FACTS a controller is introduced. The model is verified by simulation using Power System Blockset (PSB) and Simulink software.

Keywords: FACTS, Modeling, PSO, SSSC, Transmission lossreduction.

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Authors: Gianluigi De Mare, Tony Leopoldo Luigi Lenza, Rino Conte

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The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.

Keywords: Impact, compensation for financial loss, depreciation of property, railway network design, genetic algorithms.

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Authors: Taehan Lee

Abstract:

We consider a network design problem which has shortest routing restriction based on the values determined by the installed facilities on each arc. In conventional multicommodity network design problem, a commodity can be routed through any possible path when the capacity is available. But, we consider a problem in which the commodity between two nodes must be routed on a path which has shortest metric value and the link metric value is determined by the installed facilities on the link. By this routing restriction, the problem has a distinct characteristic. We present an integer programming formulation containing the primal-dual optimality conditions to the shortest path routing. We give some computational results for the model.

Keywords: Integer programming, multicommodity network design, routing, shortest path.

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Authors: S. Srinivasa Moorthy, K. Manonmani

Abstract:

The new, polymer composites consisting of e-glass fiber reinforcement with titanium oxide filler in the double bonded unsaturated polyester resin matrix were made. The glass fiber and titanium oxide reinforcement composites were made in three different fiber lengths (3cm, 5cm, and 7cm), filler content (2 wt%, 4 wt%, and 6 wt%) and fiber content (20 wt%, 40 wt%, and 60 wt%). 27 different compositions were fabricated and a sequence of experiments were carried out to determine tensile strength and impact strength. The vital influencing factors fiber length, fiber content and filler content were chosen as 3 factors in 3 levels of Taguchi’s L₉ orthogonal array. The influences of parameters were determined for tensile strength and impact strength by Analysis of variance (ANOVA) and S/N ratio. Using Artificial Neural Network (ANN) an expert system was devised to predict the properties of hybrid reinforcement GFRP composites. The predict models were experimentally proved with the maximum coincidence.

Keywords: Analysis of variance (ANOVA), Artificial neural network (ANN), Polymer composites, Taguchi’s orthogonal array.

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Authors: Ahmet Faruk Akyuz, Hasan Sakir Bilge

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Depth estimation using stereo images is a challenging problem in computer vision. Many different studies have been carried out to solve this problem. With advancing machine learning, tackling this problem is often done with neural network-based solutions. The images used in these studies are mostly in the visible spectrum. However, the need to use the Infrared (IR) spectrum for depth estimation has emerged because it gives better results than visible spectra in some conditions. At this point, we recommend using thermal-thermal (IR) image pairs for depth estimation. In this study, we used two well-known networks (PSMNet, FADNet) with minor modifications to demonstrate the viability of this idea.

Keywords: thermal stereo matching, depth estimation, deep neural networks, CNN

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Authors: A. Abdikian

Abstract:

Using the linearized quantum hydrodynamic model (QHD) and by considering the role of quantum parameter (Bohm’s potential) and electron exchange-correlation potential in conjunction with Maxwell’s equations, electromagnetic wave propagation in a single-walled carbon nanotubes was studied. The electronic excitations are described. By solving the mentioned equations with appropriate boundary conditions and by assuming the low-frequency electromagnetic waves, two general expressions of dispersion relations are derived for the transverse magnetic (TM) and transverse electric (TE) modes, respectively. The dispersion relations are analyzed numerically and it was found that the dependency of dispersion curves with the exchange-correlation effects (which have been ignored in previous works) in the low frequency would be limited. Moreover, it has been realized that asymptotic behaviors of the TE and TM modes are similar in single wall carbon nanotubes (SWCNTs). The results show that by adding the function of electron exchange-correlation potential lead to the phenomena and make to extend the validity range of QHD model. The results can be important in the study of collective phenomena in nanostructures.

Keywords: Transverse magnetic, transverse electric, quantum hydrodynamic model, electron exchange-correlation potential, single-wall carbon nanotubes.

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Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

Abstract:

A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: Transportation network, critical path, connectivity reliability, network model, Neo4J application, optimal path, critical path, edge betweenness centrality index, node betweenness centrality index, Yen’s k-shortest paths.

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