Search results for: heterogeneous combat network

Authors: Yang Xiong, Hua Cheng

Abstract:

In this paper, for the basic problem of wireless sensor network topology control and deployment, the Roesser model in rectangular grid sensor networks is presented. In addition, a general constructive realization procedure will be proposed. The procedure enables a distributed implementation of linear systems on a sensor network. A non-trivial example is illustrated.

Keywords: grid sensor networks, Roesser model, state-space realization, multidimensional systems

Procedia PDF Downloads 657

Authors: Frederic Jumelle, Kelvin So, Didan Deng

Abstract:

In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

Procedia PDF Downloads 125

Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

Procedia PDF Downloads 306

Authors: Abhisek Dash

Abstract:

This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

Procedia PDF Downloads 163

Authors: Qiuyue Peng, Vladimir Zachar

Abstract:

The application of adipose-derived stromal/stem cells (ASCs) in regenerative medicine is gaining more awareness due to their advanced translational potential and abundant source preparations. However, ASC-based translation has been confounded by high subpopulation heterogeneity, causing ambiguity about its precise therapeutic value. Some phenotypes defined by a unique combination of positive and negative surface markers have been found beneficial to the required roles. Therefore, the immunophenotypic repertoires of cultured ASCs and temporal changes of distinct subsets were investigated in this study. ASCs from three donors undergoing cosmetic liposuction were cultured in standard culturing methods, and the co-expression patterns based on the combination of selected markers at passages 1, 4, and 8 were analyzed by multi-chromatic flow cytometry. The results showed that the level of heterogeneity of subpopulations of ASCs became lower by in vitro expansion. After a few passages, most of the CD166⁺/CD274⁺/CD271⁺ based subpopulations converged to CD166 single positive cells. Meanwhile, these CD29⁺CD201⁺ double-positive cells, in combination with CD36/Stro-1 expression or without, feathered only the major epitopes and maintained prevailing throughout the whole process. This study suggested that, upon in vitro expansion, the phenotype repertoire of ASCs redistributed and stabilized in a way that cells co-expressing exclusively the strong markers remained dominant. These preliminary findings provide a general overview of the distribution of heterogeneous subsets residents within human ASCs during expansion in vitro. It is a critical step to fully characterize ASCs before clinical application, although the biological effects of heterogeneous subpopulations still need to be clarified.

Keywords: adipose-derived stromal/stem cells, heterogeneity, immunophenotype, subpopulations

Procedia PDF Downloads 114

Authors: N. Marzban, J. K. Heydarzadeh M. Pourmohammadbagher, M. H. Hatami, A. Samia

Abstract:

A nano-alumina-zirconia composite catalyst was synthesized by a simple aqueous sol-gel method using AlCl₃.6H₂O and ZrCl₄ as precursors. Thermal decomposition of the precursor and subsequent formation of γ-Al₂O₃ and t-Zr were investigated by thermal analysis. XRD analysis showed that γ-Al₂O₃ and t-ZrO₂ phases were formed at 700 °C. FT-IR analysis also indicated that the phase transition to γ-Al₂O₃ occurred in corroboration with X-ray studies. TEM analysis of the calcined powder revealed that spherical particles were in the range of 8-12 nm. The nano-alumina-zirconia composite particles were mesoporous and uniformly distributed in their crystalline phase. In order to measure the catalytic activity, esterification reaction was carried out. Biodiesel, as a renewable fuel, was formed in a continuous packed column reactor. Free fatty acid (FFA) was esterified with ethanol in a heterogeneous catalytic reactor. It was found that the synthesized γ-Al₂O₃/ZrO₂ composite had the potential to be used as a heterogeneous base catalyst for biodiesel production processes.

Keywords: nano alumina-zirconia, composite catalyst, thin film, biodiesel

Procedia PDF Downloads 233

Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

Abstract:

Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

Procedia PDF Downloads 274

Authors: Nadjat Zerf Boudjettou, Fahima Nader, Rachid Chalal

Abstract:

Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system.

Keywords: data warehouse, data mining, knowledge discovery in database, KDD, medical knowledge management, Bayesian networks

Procedia PDF Downloads 396

Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

Abstract:

The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

Procedia PDF Downloads 292

Authors: Henok Mezemr Besfat, Haftom Gebreslassie Gebregwergs

Abstract:

wireless network is one of the most important mediums of transmission of information from one device to another devices. Wireless communication has a broad range of applications, including mobile communications through cell phones and satellites, Internet of Things (IoT) connecting several devices, wireless sensor networks for traffic management and environmental monitoring, satellite communication for weather forecasting and TV without requiring any cable or wire or other electronic conductors, by using electromagnetic waves like IR, RF, satellite, etc. This paper summarizes different wireless network technologies, applications of different wireless technologies and different types of wireless networks. Generally, wireless technology will further enhance operations and experiences across sectors with continued innovation. This paper suggests different strategies that can improve wireless networks and technologies.

Keywords: wireless senser, wireless technology, wireless network, internet of things

Procedia PDF Downloads 58

Authors: Oluwaponmile David Alao

Abstract:

Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

Procedia PDF Downloads 655

Authors: İkbal Eski

Abstract:

This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

Procedia PDF Downloads 395

Authors: Safak Baykal

Abstract:

The purpose of this study is analyzing the relationship between self leadership and social capital of people with using Social Network Analysis. In this study, two aspects of social capital will be focused: bonding, homophilous social capital (BoSC) which implies better, strong, dense or closed network ties, and bridging, heterophilous social capital (BrSC) which implies weak ties, bridging the structural holes. The other concept of the study is Trust (Tr), namely interpersonal trust, willingness to ascribe good intentions to and have confidence in the words and actions of other people. In this study, the sample group, 61 people, was selected from a private firm from the defense industry. The relation between BoSC/BrSC and Tr is shown by using Social Network Analysis (SNA) and statistical analysis with Likert type-questionnaire. The results of the analysis show the Cronbach’s alpha value is 0.73 and social capital values (BoSC/BrSC) is highly correlated with Tr values of the people.

Keywords: bonding social capital, bridging social capital, trust, social network analysis (SNA)

Procedia PDF Downloads 529

Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

Abstract:

The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

Procedia PDF Downloads 45

Authors: Mary Anne Roa

Abstract:

Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

Procedia PDF Downloads 399

Authors: Alexander Hempfing

Abstract:

The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

Procedia PDF Downloads 278

Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

Abstract:

Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: open source communities, social network Analysis, time series, virtual communities

Procedia PDF Downloads 525

Authors: Zhiguo Yang, Xiangan Yue, Minglu Shao, Yue Yang, Rongjie Yan

Abstract:

It is difficult to realize deep profile control because of the small pore-throats and easy water channeling in low-permeability heterogeneous reservoir, and the traditional polymer microspheres have the contradiction between injection and plugging. In order to solve this contradiction, the controllable self-aggregating colloidal particles (CSA) containing amide groups on the surface of microspheres was prepared based on emulsion polymerization of styrene and acrylamide. The dispersed solution of CSA colloidal particles, whose particle size is much smaller than the diameter of pore-throats, was injected into the reservoir. When the microspheres migrated to the deep part of reservoir, , these CSA colloidal particles could automatically self-aggregate into large particle clusters under the action of the shielding agent and the control agent, so as to realize the plugging of the water channels. In this paper, the morphology, temperature resistance and self-aggregation properties of CSA microspheres were studied by transmission electron microscopy (TEM) and bottle test. The results showed that CSA microspheres exhibited heterogeneous core-shell structure, good dispersion, and outstanding thermal stability. The microspheres remain regular and uniform spheres at 100℃ after aging for 35 days. With the increase of the concentration of the cations, the self-aggregation time of CSA was gradually shortened, and the influence of bivalent cations was greater than that of monovalent cations. Core flooding experiments showed that CSA polymer microspheres have good injection properties, CSA particle clusters can effective plug the water channels and migrate to the deep part of the reservoir for profile control.

Keywords: heterogeneous reservoir, deep profile control, emulsion polymerization, colloidal particles, plugging characteristic

Procedia PDF Downloads 244

Authors: Younghyun Jeon, Seungjoo Maeng

Abstract:

In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point.

Keywords: ad-hoc network, MIMO, cooperative beamforming, transmit power

Procedia PDF Downloads 399

Authors: Sultan Noman Qasem

Abstract:

This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

Procedia PDF Downloads 564

Authors: Widyani Fatwa Dewi, Subroto Athor

Abstract:

In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

Procedia PDF Downloads 165

Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

Abstract:

Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

Procedia PDF Downloads 79

Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

Abstract:

The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

Procedia PDF Downloads 524

Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

Abstract:

we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

Procedia PDF Downloads 149

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

Abstract:

Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

Procedia PDF Downloads 161

Authors: Richard Draper

Abstract:

A supercomputer can be constructed from identical building blocks which are small parallel processors connected by a network referred to as the local network. The routers have unused ports which are used to interconnect the building blocks. These connections are referred to as the global network. The address space has a global and a local component (g, l). The conventional way to connect the building blocks is to connect (g, l) to (g’,l). If there are K blocks, this requires K global ports in each router. If a block is of size M, the result is a machine with KM routers having diameter two. To increase the size of the machine to 2K blocks, each router connects to only half of the other blocks. The result is a larger machine but also one with greater diameter. This is a crude description of how the network of the CRAY XC® is designed. In this paper, a family of interconnection networks using routers with K global and M local ports is defined. Coordinates are (c,d, p) and the global connections are (c,d,p)↔(c’,p,d) which swaps p and d. The network is denoted D3(K,M) and is called a Swapped Dragonfly. D3(K,M) has KM2 routers and has diameter three, regardless of the size of K. To produce a network of size KM2 conventionally, diameter would be an increasing function of K. The family of Swapped Dragonflies has other desirable properties: 1) D3(K,M) scales linearly in K and quadratically in M. 2) If L < K, D3(K,M) contains many copies of D3(L,M). 3) If L < M, D3(K,M) contains many copies of D3(K,L). 4) D3(K,M) can perform an all-to-all exchange in KM2+KM time which is only slightly more than the time to do a one-to-all. This paper makes several contributions. It is the first time that a swap has been used to define a linearly scalable family of networks. Structural properties of this new family of networks are thoroughly examined. A synchronizing packet header is introduced. It specifies the path to be followed and it makes it possible to define highly parallel communication algorithm on the network. Among these is an all-to-all exchange in time KM2+KM. To demonstrate the effectiveness of the swap properties of the network of the CRAY XC® and D3(K,16) are compared.

Keywords: all-to-all exchange, CRAY XC®, Dragonfly, interconnection network, packet switching, swapped network, topology

Procedia PDF Downloads 125

Authors: Mateusz Paszko, Ksenia Siadkowska

Abstract:

Due to the low-altitude and relatively low-speed flight, helicopters are easy targets for actual combat assets e.g. infrared-guided missiles. Current techniques aim to increase the combat effectiveness of the military helicopters. Protection of the helicopter in flight from early detection, tracking and finally destruction can be realized in many ways. One of them is cooling hot exhaust gasses, emitting from the engines to the atmosphere in special heat exchangers. Nowadays, this process is realized in ejective coolers, where strong heat and momentum exchange between hot exhaust gases and cold air ejected from atmosphere takes place. Flow effects of air, exhaust gases; mixture of those two and the heat transfer between cold air and hot exhaust gases are given by differential equations of: Mass transportation–flow continuity, ejection of cold air through expanding exhaust gasses, conservation of momentum, energy and physical relationship equations. Calculation of those processes in ejective cooler by means of classic mathematical analysis is extremely hard or even impossible. Because of this, it is necessary to apply the numeric approach with modern, numeric computer programs. The paper discussed the general usability of the Computational Fluid Dynamics (CFD) in a process of projecting the ejective exhaust gases cooler cooperating with helicopter turbine engine. In this work, the CFD calculations have been performed for ejective-based cooler cooperating with the PA W3 helicopter’s engines.

Keywords: aviation, CFD analysis, ejective-cooler, helicopter techniques

Procedia PDF Downloads 333

Authors: Xiaohan Bookman, Xiaoyan Zhu

Abstract:

The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

Procedia PDF Downloads 436

Authors: M. Reni Sagayaraj, S. Anand Gnana Selvam, R. Reynald Susainathan

Abstract:

This study analyzed a queueing system with blocking and no waiting line. The customers arrive according to a Poisson process and the service times follow exponential distribution. There are two non-identical servers in the system. The queue discipline is FCFS, and the customers select the servers on fastest server first (FSF) basis. The service times are exponentially distributed with parameters μ1 and μ2 at servers I and II, respectively. Besides, the catastrophes occur in a Poisson manner with rate γ in the system. When server I is busy or blocked, the customer who arrives in the system leaves the system without being served. Such customers are called lost customers. The probability of losing a customer was computed for the system. The explicit time dependent probabilities of system size are obtained and a numerical example is presented in order to show the managerial insights of the model. Finally, the probability that arriving customer finds system busy and average number of server busy in steady state are obtained numerically.

Keywords: queueing system, blocking, poisson process, heterogeneous servers, queue discipline FCFS, busy period

Procedia PDF Downloads 507

Authors: Kumaresh Selvakumar, Man Young Kim

Abstract:

Catalytic combustor substantially reduces emission entailing fuel-air premixing at very low equivalence ratios. The catalytic combustion of natural gas has the potential to become sufficiently active at light off temperature by the convection of heat from the catalyst surface. Only one channel is selected to investigate both the gas and surface reactions in the catalyst bed because of the honeycomb structure of the catalytic combustor. The objective of the present study is to find the methane catalytic combustion behavior inside the catalytic combustor, where the gas phase kinetics is employed by homogeneous methane combustion and surface chemistry is described with the heterogeneous catalysis of the oxidation of methane on a platinum catalyst. The reaction of the premixed mixture in the catalytic regime improves flame stability with complete combustion for lower operating flame temperature. An overview of the flow behavior is presented inside the single channel catalytic combustor including the operation of catalytic combustion with various F/A ratios and premixed inlet temperature.

Keywords: catalytic combustor, equivalence ratios, flame temperature, heterogeneous catalysis, homogeneous combustion

Procedia PDF Downloads 264