Relevant bibliographies by topics / Generalized serial network

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Generalized serial network'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Generalized serial network"

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Ji, Bin, Dezhi Zhang, Samson S. Yu, and Binqiao Zhang. "Optimally solving the generalized serial-lock scheduling problem from a graph-theory-based multi-commodity network perspective." European Journal of Operational Research 288, no. 1 (January 2021): 47–62. http://dx.doi.org/10.1016/j.ejor.2020.05.035.

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Chen, Rui, Zhenzhong Li, and Yi Lai. "Construction of a Multimodal Neuroimaging Data Fusion System and Evaluation of Mental Fatigue Using Nonlinear Analysis." Complexity 2021 (June 9, 2021): 1–13. http://dx.doi.org/10.1155/2021/8478868.

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The purpose of this research is to explore the optimization and fusion application of multimodal neuroimaging technology and analyze the evaluation method of human brain fatigue based on multimodal neuroimaging technology. Based on electroencephalogram (EEG) and fMRI (functional magnetic resonance imaging), the four-dimensional consistency of local neural activities (FOCA) and local multimodal serial analysis (LMSA) are first introduced to fuse EEG and fMRI organically. Second, the eigenspace maximal information canonical correlation analysis (emiCCA) is introduced to construct the multimodal neuroimaging data fusion system. Finally, how the brain function network is constructed is introduced. Based on the binary and the weighted brain function networks, the relationship between the human brain fatigue and the brain function network is evaluated by calculating the fractal dimension. Results demonstrate that FOCA performs well in temporal and spatial consistency indexes, and the mean level and standard deviation in the case of temporal and spatial consistency are approximately 0.45. The effect of LMSA indexes is significantly better than generalized linear models (GLMs). Under different signal-to-noise ratios (SNRs), the regression coefficient based on LMSA is much larger than the GLM estimate; the corresponding significance level is p < 0.05 ; and the maximum value of the regression coefficient appears near 0.2. In the data fusion system, the time-space matching has good results under the time accuracy based on EEG and the space accuracy based on fMRI, with the time accuracy above 88% and the space accuracy above 89%. The fractal dimension analysis based on the brain function network reveals that the weighted brain function network is more sensitive to mental fatigue. The state of human brain fatigue will make the brain function network more complicated. The fractal dimension with more network edges is around 2.2, while the fractal dimension with fewer network edges is around 1.6. The proposed data analysis and fusion system have great application potential and propose a new idea for analyzing human brain fatigue and brain aging.
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Ramlow, Lukas, and Benjamin Lindner. "Interspike interval correlations in neuron models with adaptation and correlated noise." PLOS Computational Biology 17, no. 8 (August 27, 2021): e1009261. http://dx.doi.org/10.1371/journal.pcbi.1009261.

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The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, different patterns of interspike-interval correlations have been observed and computational studies have identified spike-frequency adaptation and correlated noise as the two main mechanisms that can lead to such correlations. Analytical studies have focused on the single cases of either correlated (colored) noise or adaptation currents in combination with uncorrelated (white) noise. For low-pass filtered noise or adaptation, the serial correlation coefficient can be approximated as a single geometric sequence of the lag between the intervals, providing an explanation for some of the experimentally observed patterns. Here we address the problem of interval correlations for a widely used class of models, multidimensional integrate-and-fire neurons subject to a combination of colored and white noise sources and a spike-triggered adaptation current. Assuming weak noise, we derive a simple formula for the serial correlation coefficient, a sum of two geometric sequences, which accounts for a large class of correlation patterns. The theory is confirmed by means of numerical simulations in a number of special cases including the leaky, quadratic, and generalized integrate-and-fire models with colored noise and spike-frequency adaptation. Furthermore we study the case in which the adaptation current and the colored noise share the same time scale, corresponding to a slow stochastic population of adaptation channels; we demonstrate that our theory can account for a nonmonotonic dependence of the correlation coefficient on the channel’s time scale. Another application of the theory is a neuron driven by network-noise-like fluctuations (green noise). We also discuss the range of validity of our weak-noise theory and show that by changing the relative strength of white and colored noise sources, we can change the sign of the correlation coefficient. Finally, we apply our theory to a conductance-based model which demonstrates its broad applicability.
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Zhao, Yang. "A spectral analysis based heteroscedastic model for the estimation of value at risk." Journal of Risk Finance 19, no. 3 (May 21, 2018): 295–314. http://dx.doi.org/10.1108/jrf-01-2017-0012.

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Purpose This paper aims to focus on a better model to capture the trait of varying volatility in various financial time series, as well as to obtain reliable estimate of value at risk (VaR). Design/methodology/approach The typical methods in spectral analysis are used to obtain the sample of conditional mean, conditional variance and residual term. The generalized regression neural network is used to establish a time-varying non-linear model, and the non-parametric kernel density estimation method is applied for the estimation of VaR. Findings The proposed model is able to follow the heteroscedastic characteristic which is common in financial time series, and the estimated VaR is satisfactory. Practical implications The analysis method in this study allows the hedgers, bankers, financial analysts as well as economists to draw a better inference from financial time series. Also, relatively more precise estimate of the VaR value for a certain kind of financial asset is available. The model with its derived estimates would definitely help in developing other models. Originality/value Up-to-date, the study in literature which models financial time serial from the viewpoint of spectral analysis is rare to see. Thus, the proposed model, along with a comprehensive empirical study which reveals desirable result for the estimation of VaR would enrich the related researches at present.
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Tremblay, Antoine, Elissa Asp, Anne Johnson, Malgorzata Zarzycka Migdal, Tim Bardouille, and Aaron J. Newman. "What the Networks Tell us about Serial and Parallel Processing." Mental Lexicon 11, no. 1 (June 7, 2016): 115–60. http://dx.doi.org/10.1075/ml.11.1.06tre.

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A large literature documenting facilitative effects for high frequency complex words and phrases has led to proposals that high frequency phrases may be stored in memory rather than constructed on-line from their component parts (similarly to high frequency complex words). To investigate this, we explored language processing during a novel picture description task. Using the magneto-encephalographam (MEG) technique and generalised additive mixed-effects modelling, we characterised the effects of the frequency of use of single words as well as two-, three-, and four-word sequences (N-grams) on brain activity during the pre-production stage of unconstrained overt picture description. We expected amplitude responses to be modulated by N-gram frequency such that if N-grams were stored we would see a corresponding reduction or flattening in amplitudes as frequency increased. We found that while amplitude responses to increasing N-gram frequencies corresponded with our expectations about facilitation, the effect appeared at low frequency ranges and for single words only in the phonological network. We additionally found that high frequency N-grams elicited activity increases in some networks, which may be signs of competition or combination depending on the network. Moreover, this effect was not reliable for single word frequencies. These amplitude responses do not clearly support storage for high frequency multi-word sequences. To probe these unexpected results, we turned our attention to network topographies and the timing. We found that, with the exception of an initial ‘sentence’ network, all the networks aggregated peaks from more than one domain (e.g. semantics and phonology). Moreover, although activity moved serially from anterior ventral networks to dorsal posterior networks during processing, as expected in combinatorial accounts, sentence processing and semantic networks ran largely in parallel. Thus, network topographies and timing may account for (some) facilitative effects associated with frequency. We review literature relevant to the network topographies and timing and briefly discuss our results in relation to current processing and theoretical models.
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Maiborodin, Igor, Aleksandr Shevela, Michael Toder, Sergey Marchukov, Natalya Tursunova, Marina Klinnikova, Vitalina Maiborodina, Elena Lushnikova, and Andrew Shevela. "Multipotent Stromal Cell Extracellular Vesicle Distribution in Distant Organs after Introduction into a Bone Tissue Defect of a Limb." Life 11, no. 4 (April 1, 2021): 306. http://dx.doi.org/10.3390/life11040306.

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When administered intravenously, extracellular vesicles derived from multipotent stromal cells (MSC EVs) immediately pass through the lungs along with the blood and regularly spread to all organs. When administered intraperitoneally, they are absorbed either into the blood or into the lymph and are quickly disseminated throughout the body. The possibility of generalized spread of MSC EVs to distant organs in case of local intratissular administration remains unexplored. However, it is impossible to exclude MSC EV influence on tissues distant from the injection site due to the active or passive migration of these injected nanoparticles through the vessels. The research is based on findings obtained when studying the samples of lungs, heart, spleen, and liver of outbred rabbits of both sexes weighing 3–4 kg at various times after the injection of EVs derived from MSCs of bone marrow origin and labeled by PKH26 into an artificially created defect of the proximal condyle of the tibia. MSC EVs were isolated by serial ultracentrifugation and characterized by transmission electron microscopy and flow cytometry. After the introduction of MSC EVs into the damaged proximal condyle of the tibia of rabbits, these MSC EVs can be found most frequently in the lungs, myocardium, liver, and spleen. MSC EVs enter all of these organs with the blood flow. The lungs contained the maximum number of labeled MSC EVs; moreover, they were often associated with detritus and were located in the lumen of the alveoli. In the capillary network of various organs except the myocardium, MSC EVs are adsorbed by paravasal phagocytes; in some cases, specifically labeled small dust-like objects can be detected throughout the entire experiment—up to ten days of observation. Therefore, we can conclude that the entire body, including distant organs, is effected both by antigenic detritus, which appeared in the bloodstream after extensive surgery, and MSC EVs introduced from the outside.
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Sharon, Eran, Noam Presman, and Simon Litsyn. "Convergence analysis of generalized serial message-passing schedules." IEEE Journal on Selected Areas in Communications 27, no. 6 (August 2009): 1013–24. http://dx.doi.org/10.1109/jsac.2009.090819.

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Datsikas, Christos K., Kostas P. Peppas, Nikos C. Sagias, and George S. Tombras. "Serial relaying communications over generalized-gamma fading channels." Wireless Communications and Mobile Computing 12, no. 13 (February 3, 2011): 1191–202. http://dx.doi.org/10.1002/wcm.1047.

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Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (May 7, 2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.
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Zhang, Shiyong, and Gongliang Chen. "Micro-Trivium: A lightweight algorithm designed for radio frequency identification systems." International Journal of Distributed Sensor Networks 13, no. 2 (February 2017): 155014771769417. http://dx.doi.org/10.1177/1550147717694171.

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Distributed sensor networks have been widely applied to healthcare, environmental monitoring and management, intelligent transportation, and other fields in which network sensors collect and transmit information about their surrounding environment. Radio frequency identification technology transmits an object’s identification as a unique serial number—using radio waves as the transmission carrier—and is becoming an important building block for distributed sensor networks. However, the security of radio frequency identification systems is a major industrial concern that can significantly hinder the market growth of distributed sensor networks. Trivium is a well-known lightweight synchronous stream cipher that was submitted to the European eSTREAM project in April 2005. In this article, we generalize Trivium to the Trivium-Model algorithm and highlight that security is mainly determined by the internal state bits and the number of nonlinear terms. We propose principles for choosing parameters and generating better parameters that are feasible for low-cost radio frequency identification tags in distributed sensor networks. The new algorithm, named Micro-Trivium, requires less power and a smaller chip area than the original Trivium, which is proven using experimental data and results. Mathematical analysis shows that using Micro-Trivium is as secure as using Trivium.
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Dissertations / Theses on the topic "Generalized serial network"

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Tra, Niem-Trung L. "Comparison of Scheduling Algorithms for a Multi-Product Batch-Chemical Plant with a Generalized Serial Network." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/31036.

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Despite recent advances in computer power and the development of better algorithms, theoretical scheduling methodologies developed for batch-chemical production are seldom applied in industry (Musier & Evans 1989 and Grossmann et al. 1992). Scheduling decisions may have significant impact on overall company profitability by defining how capital is utilized, the operating costs required, and the ability to meet due dates. The purpose of this research is to compare different production scheduling methods by applying them to a real-world multi-stage, multi-product, batch-chemical production line. This research addresses the problem that the theoretical algorithms are seldom applied in industry and allows for performance analysis of several theoretical algorithms.

The research presented in this thesis focuses on the development and comparison of several scheduling algorithms. The two objectives of this research are to: 1. modify different heuristic production scheduling algorithms to minimize tardiness for a multi-product batch plant involving multiple processing stages with several out-of-phase parallel machines in each stage; and 2. compare the robustness and performance of these production schedules using a stochastic discrete event simulation of a real-world production line. The following three scheduling algorithms are compared: 1. a modified Musier and Evans scheduling algorithm (1989); 2. a modified Ku and Karimi Sequence Building Algorithm (1991); and 3. a greedy heuristic based on an earliest-due-date (EDD) policy. Musier and Evans' heuristic improvement method (1989) is applied to the three algorithms. The computation times to determine the total tardiness of each schedule are compared. Finally, all the schedules are tested for robustness and performance in a stochastic setting with the use of a discrete event simulation (DES) model. Mignon, Honkomp, and Reklaitis' evaluation techniques (1995) and Multiple Comparison of the Best are used to help determine the best algorithm.
Master of Science

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WANG, RUO-JIE, and 王若杰. "Generalized network partition method for the goal linear programming model of serial-parallel reservoir system." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/80171528712027651596.

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Book chapters on the topic "Generalized serial network"

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Kennington, Jeffery L., and Riad A. Mohamed. "An Efficient Dual Simplex Optimizer for Generalized Networks." In Operations Research/Computer Science Interfaces Series, 153–82. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4102-8_6.

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Ziavras, Sotirios. "Generalized reduced hypercube interconnection networks for massively parallel computers." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 307–25. Providence, Rhode Island: American Mathematical Society, 1995. http://dx.doi.org/10.1090/dimacs/021/21.

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Wan, Peng-Jun, and A. Pavan. "A scalable TWDM lightwave network based on generalized de Bruijn digraph." In DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 397–414. Providence, Rhode Island: American Mathematical Society, 1998. http://dx.doi.org/10.1090/dimacs/040/22.

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Selviah, David R., and Janti Shawash. "Generalized Correlation Higher Order Neural Networks for Financial Time Series Prediction." In Artificial Higher Order Neural Networks for Economics and Business, 212–49. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-897-0.ch010.

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Generalized correlation higher order neural network designs are developed. Their performance is compared with that of first order networks, conventional higher order neural network designs, and higher order linear regression networks for financial time series prediction. The correlation higher order neural network design is shown to give the highest accuracy for prediction of stock market share prices and share indices. The simulations compare the performance for three different training algorithms, stationary versus non-stationary input data, different numbers of neurons in the hidden layer and several generalized correlation higher order neural network designs. Generalized correlation higher order linear regression networks are also introduced and two designs are shown by simulation to give good correct direction prediction and higher prediction accuracies, particularly for long-term predictions, than other linear regression networks for the prediction of inter-bank lending risk Libor and Swap interest rate yield curves. The simulations compare the performance for different input data sample lag lengths.
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"Generalized Scattering Parameters of a 2-port Network." In Advanced Series in Circuits and Systems, 189–234. WORLD SCIENTIFIC, 1996. http://dx.doi.org/10.1142/9789812830869_0006.

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Diao, Qian, Jianye Lu, Wei Hu, Yimin Zhang, and Gary Bradski. "DBN Models for Visual Tracking and Prediction." In Bayesian Network Technologies, 176–93. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-141-4.ch009.

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In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.
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Butcher-Powell, Loreen Marie. "Better Securing an Infrastructure for Telework." In Information Security and Ethics, 2044–58. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch137.

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The XYZ Hardware Company, Inc. infrastructure features high volumes of sensitive and confi- dential corporate data relevant to internal and external transactions. From 1999 to the middle of 2004, XYZ has utilized the Operationally Critical Threat, Asset, and Vulnerability EvaluationSM (OCTAVESM) Model version 1e to protect its network. The OCTAVESM Model has proven to be helpful for XYZ by identifying over 198 potential security breaches. However, in 2004, when XYZ began to enhance its existing network infrastructure to include telework, 210 security breaches occurred. These breaches cost the company over $350,000 in lost profits between July and December of 2004. To safeguard their network, upper management wanted to invest the money in a series of generalized training including working ethics, virus scanning, and backing up files. However, instead, XYZ’S chief information officer (CIO) invested over $100,000 in research in order to modify their existing protection strategy, to better safeguard their new telework infrastructure by identifying its specific strengths and weakness in an effort to create more concentrated and specialized training at the root of the problem.
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Alain, Kammogne Soup Tewa, Kengne Romanic, Ahmad Taher Azar, Sundarapandian Vaidyanathan, Fotsin Hilaire Bertrand, and Ngo Mouelas Adèle. "Dynamics Analysis and Synchronization in Relay Coupled Fractional Order Colpitts Oscillators." In Advances in System Dynamics and Control, 317–56. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4077-9.ch011.

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In this chapter, the dynamics of a particular topology of Colpitts oscillator with fractional order dynamics is presented. The first part is devoted to the dynamics of the model using standard nonlinear analysis techniques including time series, bifurcation diagrams, phase space trajectories plots, and Lyapunov exponents. One of the major results of this innovative work is the numerical finding of a parameter region in which the fractional order Colpitts oscillator's circuit experiences multiple attractors' behavior. This phenomenon was not reported previously in the Colpitts circuit (despite the huge amount of related research works) and thus represents an enriching contribution to the understanding of the dynamics of Chua's oscillator. The second part of this chapter deals with the synchronization of fractional order system. Based on fractional-order Lyapunov stability theory, this chapter provides a novel method to achieve generalized and phase synchronization of two and network fractional-order chaotic Colpitts oscillators, respectively.
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Conference papers on the topic "Generalized serial network"

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Toharudin, Toni, Rezzy Eko Caraka, Gumgum Darmawan, Akbar Iskandar, Oman Somantri, Arnita, Joko Soebagyo, Noor Ell Goldameir, and S. Asmawati. "Ensemble Time Series Modified Generalized Regression Neural Network Rainfall Forecasting." In 2018 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME). IEEE, 2018. http://dx.doi.org/10.1109/bicame45512.2018.1570505191.

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Hayashi, Shogo, Akira Tanimoto, and Hisashi Kashima. "Long-Term Prediction of Small Time-Series Data Using Generalized Distillation." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851687.

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Banakar, A., and M. F. Azeem. "Generalized Wavelet Neural Network Model and its Application in Time Series Prediction." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246778.

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Al-Mahasneh, Ahmad Jobran, Sreenatha G. Anavatti, and Matthew A. Garratt. "Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8280951.

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Junior, Norberto Ritzmann, and Julio Cesar Nievola. "A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489034.

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