Search results for: computation independent model (CIM)
18528 Cellular Automata Using Fractional Integral Model
Authors: Yasser F. Hassan
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In this paper, a proposed model of cellular automata is studied by means of fractional integral function. A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. The paper discusses how using fractional integral function for representing cellular automata memory or state. The architecture of computing and learning model will be given and the results of calibrating of approach are also given.Keywords: fractional integral, cellular automata, memory, learning
Procedia PDF Downloads 40618527 Numerical Model Validation Using Durbin Method
Authors: H. Al-Hajeri
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The computation of the effectiveness of turbulence enhancement surface features, such as ribs as means of promoting mixing and hence heat transfer, has attracted the continued attention of the engineering community. In this study, the simulation of a three-dimensional cooling passage is carried out employing a number of turbulence models including Durbin model. The cooling passage consists of a square section duct whose upper and lower surfaces feature staggered cuboid ribs. The main objective of this paper is to provide comparisons of the performance of the v2-f model against other established turbulence models as implemented in the commercial CFD code Ansys Fluent. The present study demonstrates that the v2-f model can successfully capture the isothermal air flow phenomena in flow over obstacles.Keywords: CFD, cooling passage, Durbin model, turbulence model
Procedia PDF Downloads 49918526 Dynamic Fault Diagnosis for Semi-Batch Reactor Under Closed-Loop Control via Independent RBFNN
Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm
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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control
Procedia PDF Downloads 49118525 Simultaneous versus Sequential Model in Foreign Entry
Authors: Patricia Heredia, Isabel Saz, Marta Fernández
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This article proposes that the decision regarding exporting and the choice of export channel are nested and non-independent decisions. We assume that firms make two sequential decisions before arriving at their final choice: the decision to access foreign markets and the decision about the type of channel. This hierarchical perspective of the choices involved in the process is appealing for two reasons. First, it supports the idea that people have a limited analytical capacity. Managers often break down a complex decision into a hierarchical process because this makes it more manageable. Secondly, it recognizes that important differences exist between entry modes. In light of the above, the objective of this study is to test different entry mode choice processes: independent decisions and nested and non-independent decisions. To do this, the methodology estimates and compares the following two models: (i) a simultaneous single-stage model with three entry mode choices (using a multinomial logit model); ii) a two-stage model with the export decision preceding the channel decision using a sequential logit model. The study uses resource-based factors in determining these decision processes concerning internationalization and the study carries out empirical analysis using a DOC Rioja sample of 177 firms.Using the Akaike and Schwarz Information Criteria, the empirical evidence supports the existence of a nested structure, where the decision about exporting precedes the export mode decision. The implications and contributions of the findings are discussed.Keywords: sequential logit model, two-stage choice process, export mode, wine industry
Procedia PDF Downloads 1718524 A Comparison of Methods for Neural Network Aggregation
Authors: John Pomerat, Aviv Segev
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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning
Procedia PDF Downloads 15418523 Safety Approach Highway Alignment Optimization
Authors: Seyed Abbas Tabatabaei, Marjan Naderan Tahan, Arman Kadkhodai
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An efficient optimization approach, called feasible gate (FG), is developed to enhance the computation efficiency and solution quality of the previously developed highway alignment optimization (HAO) model. This approach seeks to realistically represent various user preferences and environmentally sensitive areas and consider them along with geometric design constraints in the optimization process. This is done by avoiding the generation of infeasible solutions that violate various constraints and thus focusing the search on the feasible solutions. The proposed method is simple, but improves significantly the model’s computation time and solution quality. On the other, highway alignment optimization through Feasible Gates, eventuates only economic model by considering minimum design constrains includes minimum reduce of circular curves, minimum length of vertical curves and road maximum gradient. This modelling can reduce passenger comfort and road safety. In most of highway optimization models, by adding penalty function for each constraint, final result handles to satisfy minimum constraint. In this paper, we want to propose a safety-function solution by introducing gift function.Keywords: safety, highway geometry, optimization, alignment
Procedia PDF Downloads 40518522 Learning from Dendrites: Improving the Point Neuron Model
Authors: Alexander Vandesompele, Joni Dambre
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The diversity in dendritic arborization, as first illustrated by Santiago Ramon y Cajal, has always suggested a role for dendrites in the functionality of neurons. In the past decades, thanks to new recording techniques and optical stimulation methods, it has become clear that dendrites are not merely passive electrical components. They are observed to integrate inputs in a non-linear fashion and actively participate in computations. Regardless, in simulations of neural networks dendritic structure and functionality are often overlooked. Especially in a machine learning context, when designing artificial neural networks, point neuron models such as the leaky-integrate-and-fire (LIF) model are dominant. These models mimic the integration of inputs at the neuron soma, and ignore the existence of dendrites. In this work, the LIF point neuron model is extended with a simple form of dendritic computation. This gives the LIF neuron increased capacity to discriminate spatiotemporal input sequences, a dendritic functionality as observed in another study. Simulations of the spiking neurons are performed using the Bindsnet framework. In the common LIF model, incoming synapses are independent. Here, we introduce a dependency between incoming synapses such that the post-synaptic impact of a spike is not only determined by the weight of the synapse, but also by the activity of other synapses. This is a form of short term plasticity where synapses are potentiated or depressed by the preceding activity of neighbouring synapses. This is a straightforward way to prevent inputs from simply summing linearly at the soma. To implement this, each pair of synapses on a neuron is assigned a variable,representing the synaptic relation. This variable determines the magnitude ofthe short term plasticity. These variables can be chosen randomly or, more interestingly, can be learned using a form of Hebbian learning. We use Spike-Time-Dependent-Plasticity (STDP), commonly used to learn synaptic strength magnitudes. If all neurons in a layer receive the same input, they tend to learn the same through STDP. Adding inhibitory connections between the neurons creates a winner-take-all (WTA) network. This causes the different neurons to learn different input sequences. To illustrate the impact of the proposed dendritic mechanism, even without learning, we attach five input neurons to two output neurons. One output neuron isa regular LIF neuron, the other output neuron is a LIF neuron with dendritic relationships. Then, the five input neurons are allowed to fire in a particular order. The membrane potentials are reset and subsequently the five input neurons are fired in the reversed order. As the regular LIF neuron linearly integrates its inputs at the soma, the membrane potential response to both sequences is similar in magnitude. In the other output neuron, due to the dendritic mechanism, the membrane potential response is different for both sequences. Hence, the dendritic mechanism improves the neuron’s capacity for discriminating spa-tiotemporal sequences. Dendritic computations improve LIF neurons even if the relationships between synapses are established randomly. Ideally however, a learning rule is used to improve the dendritic relationships based on input data. It is possible to learn synaptic strength with STDP, to make a neuron more sensitive to its input. Similarly, it is possible to learn dendritic relationships with STDP, to make the neuron more sensitive to spatiotemporal input sequences. Feeding structured data to a WTA network with dendritic computation leads to a significantly higher number of discriminated input patterns. Without the dendritic computation, output neurons are less specific and may, for instance, be activated by a sequence in reverse order.Keywords: dendritic computation, spiking neural networks, point neuron model
Procedia PDF Downloads 12418521 Lie Symmetry of a Nonlinear System Characterizing Endemic Malaria
Authors: Maba Boniface Matadi
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This paper analyses the model of Malaria endemic from the point of view of the group theoretic approach. The study identified new independent variables that lead to the transformation of the nonlinear model. Furthermore, corresponding determining equations were constructed, and new symmetries were found. As a result, the findings of the study demonstrate of the integrability of the model to present an invariant solution for the Malaria model.Keywords: group theory, lie symmetry, invariant solutions, malaria
Procedia PDF Downloads 10318520 Radial Basis Surrogate Model Integrated to Evolutionary Algorithm for Solving Computation Intensive Black-Box Problems
Authors: Abdulbaset Saad, Adel Younis, Zuomin Dong
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For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge.Keywords: differential evolution, engineering design, expensive computations, meta-modeling, radial basis function, optimization
Procedia PDF Downloads 39118519 A Hybrid Model for Secure Protocol Independent Multicast Sparse Mode and Dense Mode Protocols in a Group Network
Authors: M. S. Jimah, A. C. Achuenu, M. Momodu
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Group communications over public infrastructure are prone to a lot of security issues. Existing network protocols like Protocol Independent Multicast Sparse Mode (PIM SM) and Protocol Independent Multicast Dense Mode (PIM DM) do not have inbuilt security features. Therefore, any user or node can easily access the group communication as long as the user can send join message to the source nodes, the source node then adds the user to the network group. In this research, a hybrid method of salting and hashing to encrypt information in the source and stub node was designed, and when stub nodes need to connect, they must have the appropriate key to join the group network. Object oriented analysis design (OOAD) was the methodology used, and the result shows that no extra controlled bandwidth overhead cost was added by encrypting and the hybrid model was more securing than the existing PIM SM, PIM DM and Zhang secure PIM SM.Keywords: group communications, multicast, PIM SM, PIM DM, encryption
Procedia PDF Downloads 15518518 Parallel Computation of the Covariance-Matrix
Authors: Claude Tadonki
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We address the issues related to the computation of the covariance matrix. This matrix is likely to be ill conditioned following its canonical expression, thus consequently raises serious numerical issues. The underlying linear system, which therefore should be solved by means of iterative approaches, becomes computationally challenging. A huge number of iterations is expected in order to reach an acceptable level of convergence, necessary to meet the required accuracy of the computation. In addition, this linear system needs to be solved at each iteration following the general form of the covariance matrix. Putting all together, its comes that we need to compute as fast as possible the associated matrix-vector product. This is our purpose in the work, where we consider and discuss skillful formulations of the problem, then propose a parallel implementation of the matrix-vector product involved. Numerical and performance oriented discussions are provided based on experimental evaluations.Keywords: covariance-matrix, multicore, numerical computing, parallel computing
Procedia PDF Downloads 30718517 A Combination of Independent Component Analysis, Relative Wavelet Energy and Support Vector Machine for Mental State Classification
Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui
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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.Keywords: EEG, ICA, SVM, wavelet
Procedia PDF Downloads 37818516 Towards Efficient Reasoning about Families of Class Diagrams Using Union Models
Authors: Tejush Badal, Sanaa Alwidian
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Class diagrams are useful tools within the Unified Modelling Language (UML) to model and visualize the relationships between, and properties of objects within a system. As a system evolves over time and space (e.g., products), a series of models with several commonalities and variabilities create what is known as a model family. In circumstances where there are several versions of a model, examining each model individually, becomes expensive in terms of computation resources. To avoid performing redundant operations, this paper proposes an approach for representing a family of class diagrams into Union Models to represent model families using a single generic model. The paper aims to analyze and reason about a family of class diagrams using union models as opposed to individual analysis of each member model in the family. The union algorithm provides a holistic view of the model family, where the latter cannot be otherwise obtained from an individual analysis approach, this in turn, enhances the analysis performed in terms of speeding up the time needed to analyze a family of models together as opposed to analyzing individual models, one model at a time.Keywords: analysis, class diagram, model family, unified modeling language, union model
Procedia PDF Downloads 6518515 Independence and Path Independence on Cayley Digraphs of Left Groups and Right Groups
Authors: Nuttawoot Nupo, Sayan Panma
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A semigroup S is said to be a left (right) zero semigroup if S satisfies the equation xy=x (xy=y) for all x,y in S. In addition, the semigroup S is called a left (right) group if S is isomorphic to the direct product of a group and a left (right) zero semigroup. The Cayley digraph Cay(S,A) of a semigroup S with a connection set A is defined to be a digraph with the vertex set S and the arc set E(Cay(S,A))={(x,xa) | x∈S, a∈A} where A is any subset of S. All sets in this research are assumed to be finite. Let D be a digraph together with a vertex set V and an arc set E. Let u and v be two different vertices in V and I a nonempty subset of V. The vertices u and v are said to be independent if (u,v)∉E and (v,u)∉E. The set I is called an independent set of D if any two different vertices in I are independent. The independence number of D is the maximum cardinality of an independent set of D. Moreover, the vertices u and v are said to be path independent if there is no dipath from u to v and there is no dipath from v to u. The set I is called a path independent set of D if any two different vertices in I are path independent. The path independence number of D is the maximum cardinality of a path independent set of D. In this research, we describe a lower bound and an upper bound of the independence number of Cayley digraphs of left groups and right groups. Some examples corresponding to those bounds are illustrated here. Furthermore, the exact value of the path independence number of Cayley digraphs of left groups and right groups are also presented.Keywords: Cayley digraphs, independence number, left groups, path independence number, right groups
Procedia PDF Downloads 22618514 Artificial Reproduction System and Imbalanced Dataset: A Mendelian Classification
Authors: Anita Kushwaha
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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining
Procedia PDF Downloads 26818513 On Cold Roll Bonding of Polymeric Films
Authors: Nikhil Padhye
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Recently a new phenomenon for bonding of polymeric films in solid-state, at ambient temperatures well below the glass transition temperature of the polymer, has been reported. This is achieved by bulk plastic compression of polymeric films held in contact. Here we analyze the process of cold-rolling of polymeric films via finite element simulations and illustrate a flexible and modular experimental rolling-apparatus that can achieve bonding of polymeric films through cold-rolling. Firstly, the classical theory of rolling a rigid-plastic thin-strip is utilized to estimate various deformation fields such as strain-rates, velocities, loads etc. in rolling the polymeric films at the specified feed-rates and desired levels of thickness-reduction(s). Predicted magnitudes of slow strain-rates, particularly at ambient temperatures during rolling, and moderate levels of plastic deformation (at which Bauschinger effect can be neglected for the particular class of polymeric materials studied here), greatly simplifies the task of material modeling and allows us to deploy a computationally efficient, yet accurate, finite deformation rate-independent elastic-plastic material behavior model (with inclusion of isotropic-hardening) for analyzing the rolling of these polymeric films. The interfacial behavior between the roller and polymer surfaces is modeled using Coulombic friction; consistent with the rate-independent behavior. The finite deformation elastic-plastic material behavior based on (i) the additive decomposition of stretching tensor (D = De + Dp, i.e. a hypoelastic formulation) with incrementally objective time integration and, (ii) multiplicative decomposition of deformation gradient (F = FeFp) into elastic and plastic parts, are programmed and carried out for cold-rolling within ABAQUS Explicit. Predictions from both the formulations, i.e., hypoelastic and multiplicative decomposition, exhibit a close match. We find that no specialized hyperlastic/visco-plastic model is required to describe the behavior of the blend of polymeric films, under the conditions described here, thereby speeding up the computation process .Keywords: Polymer Plasticity, Bonding, Deformation Induced Mobility, Rolling
Procedia PDF Downloads 18218512 Extracting Opinions from Big Data of Indonesian Customer Reviews Using Hadoop MapReduce
Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya
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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews
Procedia PDF Downloads 19718511 Modeling the Impacts of Road Construction on Lands Values
Authors: Maha Almumaiz, Harry Evdorides
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Change in land value typically occurs when a new interurban road construction causes an increase in accessibility; this change in the adjacent lands values differs according to land characteristics such as geographic location, land use type, land area and sale time (appraisal time). A multiple regression model is obtained to predict the percent change in land value (CLV) based on four independent variables namely land distance from the constructed road, area of land, nature of land use and time from the works completion of the road. The random values of percent change in land value were generated using Microsoft Excel with a range of up to 35%. The trend of change in land value with the four independent variables was determined from the literature references. The statistical analysis and model building process has been made by using the IBM SPSS V23 software. The Regression model suggests, for lands that are located within 3 miles as the straight distance from the road, the percent CLV is between (0-35%) which is depending on many factors including distance from the constructed road, land use, land area and time from works completion of the new road.Keywords: interurban road, land use types, new road construction, percent CLV, regression model
Procedia PDF Downloads 26318510 Model Order Reduction Using Hybrid Genetic Algorithm and Simulated Annealing
Authors: Khaled Salah
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Model order reduction has been one of the most challenging topics in the past years. In this paper, a hybrid solution of genetic algorithm (GA) and simulated annealing algorithm (SA) are used to approximate high-order transfer functions (TFs) to lower-order TFs. In this approach, hybrid algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. Compared to conventional mathematical methods that have been used to obtain a reduced order model of high order complex models, our proposed method provides better results in terms of reducing run-time. Thus, the proposed technique could be used in electronic design automation (EDA) tools.Keywords: genetic algorithm, simulated annealing, model reduction, transfer function
Procedia PDF Downloads 14018509 The Intersection/Union Region Computation for Drosophila Brain Images Using Encoding Schemes Based on Multi-Core CPUs
Authors: Ming-Yang Guo, Cheng-Xian Wu, Wei-Xiang Chen, Chun-Yuan Lin, Yen-Jen Lin, Ann-Shyn Chiang
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With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Driver/Neuron images. In order to solve this problem, the intersection/union region for a set of images should be computed at first, then a comparison work is used to calculate the similarities between the region and other images. In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each image as a one-dimensional structure. Then, the intersection/union region from these images can be computed by using the compare operations, Boolean operators and lookup table method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by the Boolean scheme is the best than that by others; in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 12 based on 16 CPUs. This work was supported by the Ministry of Science and Technology under the grant MOST 106-2221-E-182-070.Keywords: Drosophila driver image, Drosophila neuron images, intersection/union computation, parallel processing, OpenMP
Procedia PDF Downloads 23218508 Deep Reinforcement Learning-Based Computation Offloading for 5G Vehicle-Aware Multi-Access Edge Computing Network
Authors: Ziying Wu, Danfeng Yan
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Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network
Procedia PDF Downloads 10618507 Web Search Engine Based Naming Procedure for Independent Topic
Authors: Takahiro Nishigaki, Takashi Onoda
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In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.Keywords: independent topic analysis, topic extraction, topic naming, web search engine
Procedia PDF Downloads 11618506 An Improved Method to Compute Sparse Graphs for Traveling Salesman Problem
Authors: Y. Wang
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The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is O(CNmaxn2) where C is the iterations, Nmax is the maximum number of frequency quadrilaterals containing each edge and n is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5n edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced.Keywords: frequency quadrilateral, iterative algorithm, sparse graph, traveling salesman problem
Procedia PDF Downloads 22618505 An Efficient Book Keeping Strategy for the Formation of the Design Matrix in Geodetic Network Adjustment
Authors: O. G. Omogunloye, J. B. Olaleye, O. E. Abiodun, J. O. Odumosu, O. G. Ajayi
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The focus of the study is to proffer easy formulation and computation of least square observation equation’s design matrix by using an efficient book keeping strategy. Usually, for a large network of many triangles and stations, a rigorous task is involved in the computation and placement of the values of the differentials of each observation with respect to its station coordinates (latitude and longitude), in their respective rows and columns. The efficient book keeping strategy seeks to eliminate or reduce this rigorous task involved, especially in large network, by simple skillful arrangement and development of a short program written in the Matlab environment, the formulation and computation of least square observation equation’s design matrix can be easily achieved.Keywords: design, differential, geodetic, matrix, network, station
Procedia PDF Downloads 34818504 Using Bidirectional Encoder Representations from Transformers to Extract Topic-Independent Sentiment Features for Social Media Bot Detection
Authors: Maryam Heidari, James H. Jones Jr.
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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.Keywords: bot detection, natural language processing, neural network, social media
Procedia PDF Downloads 11018503 Motion Estimator Architecture with Optimized Number of Processing Elements for High Efficiency Video Coding
Authors: Seongsoo Lee
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Motion estimation occupies the heaviest computation in HEVC (high efficiency video coding). Many fast algorithms such as TZS (test zone search) have been proposed to reduce the computation. Still the huge computation of the motion estimation is a critical issue in the implementation of HEVC video codec. In this paper, motion estimator architecture with optimized number of PEs (processing element) is presented by exploiting early termination. It also reduces hardware size by exploiting parallel processing. The presented motion estimator architecture has 8 PEs, and it can efficiently perform TZS with very high utilization of PEs.Keywords: motion estimation, test zone search, high efficiency video coding, processing element, optimization
Procedia PDF Downloads 35618502 Parallel Evaluation of Sommerfeld Integrals for Multilayer Dyadic Green's Function
Authors: Duygu Kan, Mehmet Cayoren
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Sommerfeld-integrals (SIs) are commonly encountered in electromagnetics problems involving analysis of antennas and scatterers embedded in planar multilayered media. Generally speaking, the analytical solution of SIs is unavailable, and it is well known that numerical evaluation of SIs is very time consuming and computationally expensive due to the highly oscillating and slowly decaying nature of the integrands. Therefore, fast computation of SIs has a paramount importance. In this paper, a parallel code has been developed to speed up the computation of SI in the framework of calculation of dyadic Green’s function in multilayered media. OpenMP shared memory approach is used to parallelize the SI algorithm and resulted in significant time savings. Moreover accelerating the computation of dyadic Green’s function is discussed based on the parallel SI algorithm developed.Keywords: Sommerfeld-integrals, multilayer dyadic Green’s function, OpenMP, shared memory parallel programming
Procedia PDF Downloads 24318501 Toward a Characteristic Optimal Power Flow Model for Temporal Constraints
Authors: Zongjie Wang, Zhizhong Guo
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While the regular optimal power flow model focuses on a single time scan, the optimization of power systems is typically intended for a time duration with respect to a desired objective function. In this paper, a temporal optimal power flow model for a time period is proposed. To reduce the computation burden needed for calculating temporal optimal power flow, a characteristic optimal power flow model is proposed, which employs different characteristic load patterns to represent the objective function and security constraints. A numerical method based on the interior point method is also proposed for solving the characteristic optimal power flow model. Both the temporal optimal power flow model and characteristic optimal power flow model can improve the systems’ desired objective function for the entire time period. Numerical studies are conducted on the IEEE 14 and 118-bus test systems to demonstrate the effectiveness of the proposed characteristic optimal power flow model.Keywords: optimal power flow, time period, security, economy
Procedia PDF Downloads 44418500 Building 1-Well-Covered Graphs by Corona, Join, and Rooted Product of Graphs
Authors: Vadim E. Levit, Eugen Mandrescu
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A graph is well-covered if all its maximal independent sets are of the same size. A well-covered graph is 1-well-covered if deletion of every vertex of the graph leaves it well-covered. It is known that a graph without isolated vertices is 1-well-covered if and only if every two disjoint independent sets are included in two disjoint maximum independent sets. Well-covered graphs are related to combinatorial commutative algebra (e.g., every Cohen-Macaulay graph is well-covered, while each Gorenstein graph without isolated vertices is 1-well-covered). Our intent is to construct several infinite families of 1-well-covered graphs using the following known graph operations: corona, join, and rooted product of graphs. Adopting some known techniques used to advantage for well-covered graphs, one can prove that: if the graph G has no isolated vertices, then the corona of G and H is 1-well-covered if and only if H is a complete graph of order two at least; the join of the graphs G and H is 1-well-covered if and only if G and H have the same independence number and both are 1-well-covered; if H satisfies the property that every three pairwise disjoint independent sets are included in three pairwise disjoint maximum independent sets, then the rooted product of G and H is 1-well-covered, for every graph G. These findings show not only how to generate some more families of 1-well-covered graphs, but also that, to this aim, sometimes, one may use graphs that are not necessarily 1-well-covered.Keywords: maximum independent set, corona, concatenation, join, well-covered graph
Procedia PDF Downloads 19918499 Exploiting Non-Uniform Utility of Computing: A Case Study
Authors: Arnab Sarkar, Michael Huang, Chuang Ren, Jun Li
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The increasing importance of computing in modern society has brought substantial growth in the demand for more computational power. In some problem domains such as scientific simulations, available computational power still sets a limit on what can be practically explored in computation. For many types of code, there is non-uniformity in the utility of computation. That is not every piece of computation contributes equally to the quality of the result. If this non-uniformity is understood well and exploited effectively, we can much more effectively utilize available computing power. In this paper, we discuss a case study of exploring such non-uniformity in a particle-in-cell simulation platform. We find both the existence of significant non-uniformity and that it is generally straightforward to exploit it. We show the potential of order-of-magnitude effective performance gain while keeping the comparable quality of output. We also discuss some challenges in both the practical application of the idea and evaluation of its impact.Keywords: approximate computing, landau damping, non uniform utility computing, particle-in-cell
Procedia PDF Downloads 253