China 
Africa 
Explore chapters and articles related to this topic
Cryptography based on chaotic and unsynchronized elements of a network
Published in Marcio Eisencraft, Romis Attux, Ricardo Suyama, Chaotic Signals in Digital Communications, 2018
Romeu M. Szmoski, Fabiano A. S. Ferrari, Sandro E. de S. Pinto, Ricardo L. Vian, Murilo S. Baptista
A different approach to secure communication using coupled chaotic systems was presented by Hung and Hu [11]. In their method, a binary message is codified considering the coupling direction between chaotic maps on a ring. The receiver decodes the message, determining the transfer entropy [22] between succeeding maps. Indeed, for any interacting system, the transfer entropy can be used for determining which variables influence the dynamics of each other. The novel feature of this method is that it does not require synchronization for transmitting data; it only requires the determination of the transfer entropy. One inconvenience is that, as this quantity is statistically defined, many observations from the dynamics of the maps are necessary in order to decode the message. The observation interval needed by the receiver to determine the transfer entropy between the maps was taken as the relaxation time [11]. Therefore, the transmission of information through this mechanism requires operating times that are multiples of the relaxation time.
Assessing Complexity and Causality in Heart Period Variability through a Model-Free Data-Driven Multivariate Approach
Published in Herbert F. Jelinek, David J. Cornforth, Ahsan H. Khandoker, ECG Time Series Variability Analysis, 2017
Alberto Porta, Luca Faes, Giandomenico Nollo, Anielle C. M. Takahashi, Aparecida M. Catai
We make reference to Porta et al. (2014) for the description of the model-free data-driven multivariate framework for the assessment of causality indexes from SAP to HP along the cardiac baroreflex and from RESP to HP along the cardiopulmonary pathway given the universe of knowledge Ω={HP,SAP,RESP}. Two approaches were considered (Porta et al., 2014), based on the notion of Granger causality (Granger, 1980) and transfer entropy (Schreiber, 2000).
Feature-Based Time-Series Analysis
Published in Guozhu Dong, Huan Liu, Feature Engineering for Machine Learning and Data Analytics, 2018
While the time series described above are the result of a single measurement taken repeatedly through time, or univariate time series, measurements are frequently made from multiple parts of a system simultaneously, yielding multivariate time series. Examples of multivariate time series include measurements of the activity dynamics of multiple brain regions through time, or measuring the air temperature, air pressure, and humidity levels together through time. Techniques have been developed to model and understand multivariate time series, and infer models of statistical associations between different parts of a system that may explain its multivariate dynamics. Methods for characterizing inter-relationships between time series are vast, including the simple measures of statistical dependencies, like linear cross correlation, mutual information, and to infer causal (directed) relationships using methods like transfer entropy and Granger causality [82]. A range of information-theoretic methods for characterizing time series, particularly the dynamics of information transfer between time series, are described and implemented in the excellent Java Information Dynamics Toolkit (JIDT) [53]. Feature-based representations of multivariate systems can include both features of individual time series, and features of inter-relationships between (e.g., pairs of) time series. However, in this chapter we focus on individual univariate time series sampled uniformly through time (that can be represented as ordered vectors, xi $ {x_i} $ ).
An integrated CRN-SVR approach for the quality consistency improvement in a diesel engine assembly process
Published in International Journal of Computer Integrated Manufacturing, 2023
Yan-Ning Sun, Qun-Long Chen, Jin-Hua Hu, Hong-Wei Xu, Wei Qin, Xiao-Xiao Shen, Zi-Long Zhuang
Yuan and Qin (Yuan and Qin 2014) diagnosed the vibration sources and propagation paths that cause oscillations in closed-loop control systems without the need to establish a system mechanism model. By combining Granger causality and topology networks, the propagation analysis method is proposed for oscillatory disturbances in an industrial sheet machine (Landman et al. 2014). Keskin and Aste (Keskin and Aste 2020) confirmed that the Granger causality analysis method is only suitable for Gaussian distribution data and can only analyze the linear causality between variables. Thus, they proposed a nonlinear transfer entropy method and proved that Granger causality and transfer entropy are equivalent to Gaussian distribution data. Nevertheless, the transfer entropy is sensitive to the selection of parameters. The algorithm has a large number of calculations, so it is not easy to directly apply it to the highly complex diesel engine assembly process.
Entropy production evaluation of a diabatic conical funnel with surface radiation in an IRS device
Published in Numerical Heat Transfer, Part A: Applications, 2023
Arnab Mukherjee, Vikrant Chandrakar, Jnana Ranjan Senapati
Using Bejan’s entropy production formula Eq. (15) [14], the local entropy production model Eqs. (6) and (7) are justified. Heat transfer entropy generation is the first term in Eq. (15)'s right-hand side, while friction entropy generation is the second. Eqs. (35) and (36), respectively, provide the equations for the Nusselt number (Nu) and Darcy friction factor (f) [40, 41]. where D represents hydraulic diameter, h represents the fluid’s average heat transfer coefficient, q‘ represents average heat flux per unit tube length, denotes fluid density, denotes fluid thermal conductivity, L represents tube length, represents the pressure drop between the inlet and outlet, V represents mean fluid velocity, and m represents mass flow rate. The outcomes of the local entropy production model and Bejan’s formulae for a smooth duct with a fixed temperature condition (Twall = 660 K) are depicted in Figure 4. The findings demonstrate that there is a significant amount of agreement and that the mistakes are within 5%.
Entropy generation analysis of fully-developed turbulent heat transfer flow in inward helically corrugated tubes
Published in Numerical Heat Transfer, Part A: Applications, 2018
Wei Wang, Yaning Zhang, Jian Liu, Zan Wu, Bingxi Li, Bengt Sundén
Bejan’s entropy generation formula Eq. (24) [41] is used to validate the local entropy generation model Eqs. (16) and (18). In the right-hand side of Eq. (24), the first term is heat transfer entropy generation, and the second term is friction entropy generation. The expressions for the Nusselt number (Nu) and Darcy friction factor (f) [42,43] are given by Eqs. (25) and (26), respectivelywhere q′ denotes average heat flux per unit tube length, λ denotes fluid thermal conductivity, ρ denotes fluid density, D denotes hydraulic diameter, m denotes mass flow rate, h denotes average heat transfer coefficient of the fluid, L denotes tube length, V denotes mean fluid velocity, and △P denotes the pressure drop between the inlet and outlet.