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This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

Renewable resources are starting to constitute a growing portion of the total generation mix of the power system. A key difference between renewables and traditional generators is that many renewable resources are managed by individuals, especially in the distribution system. In this paper, we study the capacity investment and pricing problem, where multiple renewable producers compete in a decentralized market. It is known that most deterministic capacity games tend to result in very inefficient equilibria, even when there are a large number of similar players. In contrast, we show that due to the inherent randomness of renewable resources, the equilibria in our capacity game becomes efficient as the number of players grows and coincides with the centralized decision from the social planner's problem. This result provides a new perspective on how to look at the positive influence of randomness in a game framework as well as its contribution to resource planning, scheduling, and bidding. We validate our results by simulation studies using real world data., Appears in IEEE Transactions on Sustainable Computing

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a source-aware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method.

Morse complexes are gradient-based topological descriptors with close connections to Morse theory. They are widely applicable in scientific visualization as they serve as important abstractions for gaining insights into the topology of scalar fields. Data uncertainty inherent to scalar fields due to randomness in their acquisition and processing, however, limits our understanding of Morse complexes as structural abstractions. We, therefore, explore uncertainty visualization of an ensemble of 2D Morse complexes that arises from scalar fields coupled with data uncertainty. We propose several statistical summary maps as new entities for quantifying structural variations and visualizing positional uncertainties of Morse complexes in ensembles. Specifically, we introduce three types of statistical summary maps – the probabilistic map, the significance map, and the survival map – to characterize the uncertain behaviors of gradient flows. We demonstrate the utility of our proposed approach using wind, flow, and ocean eddy simulation datasets.

As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

In this work, two practical concepts related to private information retrieval (PIR) are introduced and coined full support-rank PIR and strongly linear PIR. Being of full support-rank is a technical, yet natural condition required to prove a converse result for a capacity expression and satisfied by almost all currently known capacity-achieving schemes, while strong linearity is a practical requirement enabling implementation over small finite fields with low subpacketization degree. Then, the capacity of MDS-coded, linear, full support-rank PIR in the presence of colluding servers is derived, as well as the capacity of symmetric, linear PIR with colluding, adversarial, and nonresponsive servers for the recently introduced concept of matched randomness. This positively settles the capacity conjectures stated by Freij-Hollanti et al. and Tajeddine et al. in the presented cases. It is also shown that, further restricting to strongly-linear PIR schemes with deterministic linear interference cancellation, the so-called star product scheme proposed by Freij-Hollanti et al. is essentially optimal and induces no capacity loss.

This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with rotational invariance. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A “direct” approach to the simulation, where one first generates a dense $n \times n$ matrix from the ensemble, requires at least $\mathcal {O}(n^{2})$ resource in space and time. The HD algorithm overcomes this $\mathcal {O}(n^{2})$ bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are $\mathcal {O}(nT)$ and $\mathcal {O}(nT^{2})$ , respectively, with $T$ being the number of iterations. When $T \ll n$ , which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.

Non-ideal models have gained considerable attention in the design stage for evaluating product functionality and performance considering the realistic conditions of the product surface. The performance evaluation generally involves deriving the statistical distributions of certain key characteristics, such as gaps and distances, based on numerous assembly process simulations, thus necessitating a large number of samples of non-ideal surfaces. The traditional practice for generating such samples is to model the systematic and random deviations of each point on the part surface. It is difficult, however, to represent the randomness of the real surface formation mechanism using this point-wise approach, thus leading to significant computational cost. As a result, this technique hinders the wider adoption of specified surface randomness representations in performance simulation and analysis. The deviation modes that feature non-ideal surfaces are dominated by manufacturing error factors, which could be systematically represented by corresponding mode functions. Furthermore, the variations in the manufacturing process inevitably cause randomness in surface deviations. In this paper, this randomness is defined as the uncertainty of non-ideal surfaces and reflected by the randomness of deviation mode function coefficients. A conceptual non-ideal surface error model that characterises the uncertainty of non-ideal surfaces through the random distributions of mode function coefficients as represented in phase space is therefore proposed. The phase space is a multi-dimensional space, in which each point denotes a group of mode function coefficients that determines a certain non-ideal surface sample. Following the distribution law of coefficients, a number of non-ideal surface samples can be generated by phase space sampling. This method reduces the problem from modelling an infinite number of surface points to modelling a finite coefficient dimension of points in the compact phase space. It thus enables the efficient modelling of non-ideal surfaces that reflect realistic errors resulting from the manufacturing process and facilitates the product performance analysis under the effect of surface randomness. To verify the method, a case study involving a tapered surface manufactured by the taper-turning process is presented. The phase space and error model are discussed with respect to typical manufacturing error factors in this process.

Compressed sensing (CS) ensures the recovery of sparse vectors from a number of randomized measurements proportional to their sparsity. The initial theory considers discretized domains, and the randomness makes the physical positions of the grid nodes irrelevant. Most imaging devices, however, operate over some continuous physical domain, and it makes sense to consider Dirac masses with arbitrary positions. In this article, we consider such a continuous setup and analyze the performance of the BLASSO algorithm, which is the continuous extension of the celebrated LASSO $$\ell ^1$$ regularization method. This approach is appealing from a numerical perspective because it avoids to discretize the domain of interest. Previous works considered translation-invariant measurements, such as randomized Fourier coefficients, in which it makes clear that the discrete theory should be extended by imposing a minimum distance separation constraint (often called “Rayleigh limit”) between the Diracs. These prior works, however, rule out many domains and sensing operators of interest, which are not translation invariant. This includes, for instance, Laplace measurements over the positive reals and Gaussian mixture models over the mean-covariance space. Our theoretical advances crucially rely on the introduction of a canonical metric associated with the measurement operator, which is the so-called Fisher geodesic distance. In the case of Fourier measurements, one recovers the Euclidean metric, but this metric can cope with arbitrary (possibly non-translation invariant) domains. Furthermore, it is naturally invariant under joint reparameterization of both the sensing operator and the Dirac locations. Our second and main contribution shows that if the Fisher distance between spikes is larger than a Rayleigh separation constant, then the BLASSO recovers in a stable way a stream of Diracs, provided that the number of measurements is proportional (up to log factors) to the number of Diracs. We measure the stability using an optimal transport distance constructed on top of the Fisher geodesic distance. Our result is (up to log factor) sharp and does not require any randomness assumption on the amplitudes of the underlying measure. Our proof technique relies on an infinite-dimensional extension of the so-called golfing scheme which operates over the space of measures and is of general interest.

SiC/SiC ceramic matrix composites (CMCs) exhibit significant randomness of damage processes under load. In this study, a simplified simulation method was established based on the macroscopic damage constitutive model, characteristics of fewer test samples, and complex data sources for determining the randomness of the material behavior. Moreover, the established model was verified by introducing an uncertainty parameter to the constitutive model. By considering a typical high-dimensional nonlinear function as a numerical example, the reliability and error of the characterization methods were compared with specimens and studied in terms of the extreme-value interval, tolerance interval, evidence theory, and fuzzy set method with the distribution characteristics of single and mixed parameters. In conjunction with the sensitivity analysis, the randomness of the mechanical behavior was more evident at the damage stage, and the uncertainty of material behavior relied on the loading state. In addition, the correction method of the constitutive parameters and formal errors of the model were established using the Bayesian theorem to acquire additional data. Thereafter, the proposed method was employed for quantifying the uncertainty in the strength of the CMC turbine rotor blade. The determinacy results revealed that the failure risk could be more effectively evaluated by the strength analysis considering the uncertainty of the model, thus providing guidance toward structural design improvement aligned with engineering practice.

Given the randomness of corrosion and the variability of the constitutive model under corrosion conditions, the high-strength steel wire is selected as the basic component, and the constitutive model of steel wire is obtained through the tensile test of high-strength steel wire under different corrosion degrees. The innovation of this paper is the generation of random corrosion pits in numerical simulations. The random corrosion model of steel wire is developed by python, and the constitutive model of steel wire under different corrosion degrees is developed by FORTRAN, which is embedded in ABAQUS. The failure modes of high-strength steel wire under different random corrosion were analyzed. The results show that corrosion has little effect on elastic modulus and yield strength of steel wire. The ultimate strength and fracture strain of steel wire are decreased due to corrosion. The steel wire becomes brittle, and the breaking force is greatly reduced.

Device-independent quantum key distribution (DIQKD) is one of the most challenging tasks in quantum cryptography. The protocols and their security are based on the existence of Bell inequalities and the ability to violate them by measuring entangled states. We study the entanglement needed for DIQKD protocols in two different ways. Our first contribution is the derivation of upper bounds on the key rates of CHSH-based DIQKD protocols in terms of the violation of the inequality; this sets an upper limit on the possible DI key extraction rate from states with a given violation. Our upper bound improves on the previously known bound of Kaur et al. Our second contribution is the initiation of the study of the role of bound entangled states in DIQKD. We present a revised Peres conjecture stating that such states cannot be used as a resource for DIQKD. We give a first piece of evidence for the conjecture by showing that the bound entangled state found by Vertesi and Brunner, even though it can certify DI randomness, cannot be used to produce a key using protocols analogous to the well-studied CHSH-based DIQKD protocol., 17 pages, 3 figures

International audience; The design of glitch-resistant higher-order masking schemes is an important challenge in cryptographic engineering. A recent work by Moos et al. (CHES 2019) showed that most published schemes (and all efficient ones) exhibit local or composability flaws at high security orders, leaving a critical gap in the literature on hardware masking. In this paper, we first extend the simulatability framework of Belaïd et al. (EUROCRYPT 2016) and prove that a compositional strategy that is correct without glitches remains valid with glitches. We then use this extended framework to prove the first masked gadgets that enable trivial composition with glitches at arbitrary orders. We show that the resulting "Hardware Private Circuits" approach the implementation efficiency of previous (flawed) schemes. We finally investigate how trivial composition can serve as a basis for a tool that allows verifying full masked hardware implementations (e.g., of complete block ciphers) at any security order from their HDL code. As side products, we improve the randomness complexity of the best published refreshing gadgets, show that some S-box representations allow latency reductions and confirm practical claims based on implementation results.

In the literature, different risk criteria are proposed for risk-averse decision-making, but it is often unclear which risk criteria should be used and what their differences may be. This study investigates this problem when a risk-averse, make-to-order firm decides its pricing and sales effort, two factors affecting demand. We consider alternative risk criteria commonly used in the literature, namely expected utility theory (EUT), mean-variance (MV), mean-semideviation (MS), value-at-risk (VaR), conditional value-at-risk (CVaR), and loss aversion (LA). We find that all of these criteria lead to similar qualitative behaviors regarding how risk-/loss-averse decisions deviate from risk-neutral ones. A particularly interesting result is that the form of demand randomness, i.e., additive or multiplicative, can completely reverse the effect of risk/loss aversion in certain cases. We also find that the risk aversion models, i.e., EUT, MV, MS, VaR, and CVaR, are essentially equivalent in inducing the same set of optimal solutions under adverse market states. However, loss aversion is a mild version of risk aversion in that the LA model can only lead to a subset of the solutions generated by other models. These results may not be valid for other applications, such as the newsvendor pricing problem, so we can conclude that the relationship between alternative risk criteria is case-sensitive and should be evaluated more carefully.

Gas-solid circulating fluidized beds (CFBs) are widely used as reactors in industry with performances heavily relying on the interaction between gas and particles. In a CFB, some particles stay dispersed, while other particles aggregate to form dense and large clusters. As the gas-particle interaction is simple for dispersed particles, the prediction of reactor performances calls for a thorough analysis of the gas-cluster interaction. In the literature, cluster properties have been characterized extensively. However, how the gas interacts with clusters has not been quantified systematically yet. In this work, the gas-cluster interaction was quantified numerically by characterizing the penetrating gas flow into clusters. With 99 simulations, influences of the particle randomness, cluster solids holdup, cluster diameter, particle diameter, superficial gas velocity and fluid properties on the gas-cluster interaction were investigated systematically. Then, an empirical correlation was proposed to quantify the gas-cluster interaction, significantly contributing to the design and optimization of CFBs.

Multicriteria group decision-making (MCGDM), with the strong uncertainty and randomness, has always been a hotspot in the world. The chief purpose of the paper is to address the problem with Atanassov’s interval-valued intuitionistic fuzzy sets (AIVIFSs), Z-numbers, and trapezium clouds. First, some related concepts and former operators of AIVIFSs, Z-numbers, and trapezium clouds are reviewed, meanwhile, AIVIFSs and Z-numbers are synthesized to come up with a novel linguistic expression. Then, Z-trapezium-trapezium cloud (ZTTC) is proposed to quantify the linguistic evaluation information to avoid excessive computation caused by traditional methods. Later, a new approach of calculating the objective weight vector is presented based on entropy weight method (EWM). To take the huge advantages of technique for order preference by similarity to ideal solution (TOPSIS) method in ranking, 2-norm in mathematical theory is applied to derive a way of calculating the distance between different ZTTCs. Finally, an example about the grade assessment of coronavirus Disease 2019 (COVID-19) is given. For further confirming the validity and feasibility, sensitivity analysis and comparison with other methods are conducted.

To solve constrained optimization problems (COPs), it is crucial to guide the infeasible solution to a feasible region. Gradient-based repair (GR) is a successful repair strategy, where the forward difference is often used to estimate the gradient. However, GR has major deficiencies. First, it is difficult to deal with individuals falling into the local optima. Second, large amounts of fitness evaluations are required to estimate the gradient. In this paper, we proposed a new repair strategy, random direction repair (RDR). RDR generates a set of random directions, and calculates the repair direction and the repair step size of infeasible individual to reduce its constraint violation. Since the introduction of randomness, RDR could deal with individuals falling into the local optima. Furthermore, RDR only requires a few number of fitness evaluation. To demonstrate the performance of RDR, RDR was embedded into two state-of-the-art evolutionary continuous constrained optimization algorithms, tested on the Congress on Evolutionary Computation 2017 constrained real-parameter optimization benchmark. Experimental results demonstrated that RDR combined with evolutionary algorithms are highly competitive.

Nonlinear periodic structures can present abundant nonlinear wave physics. The model consisting of periodic bistable oscillators (i.e., the bistable periodic structure) is essentially different from those nonlinear periodic systems consisting of monostable oscillators due to multiple equilibria in bistable periodic structure. Despite the extensive attention received, properties of harmonic and shock wave propagation in bistable periodic structure, especially the randomness and tunability behind regularity, have not been fully understood. This article reports the answers based on numerical method. We consider the varying trends of the band gap, vibration center, wave amplitude, and transmission and show their effects on energy transport. We find that the snap-through behavior always presents local intrinsic randomness with the regularity in whole, that is, it does not happen in sequence. For both harmonic and shock wave, most energy is localized inside the snap-through oscillators that changes the regularity for energy transport and is meaningful for shock wave protection. Bistable periodic structure can present very low-frequency and broadband wave attenuation by shifting the initial frequency of the band gap to nearly zero through tuning the wave amplitude to a critical value, which offers dynamic tunability. The damping and intensity of the shock pulse have significant effects on the shock wave propagation. This work provides guidance for the design and application of bistable periodic structure for elastic wave attenuation and shock wave protection.

Surface topography is a critical quality characteristic of many products and manufacturing processes. Various defects commonly appear on the topography of finished products in spatial patterns after or during manufacturing. These defects are difficult to be identified using traditional monitoring approaches because of the complex structure of topographic data. This article develops a novel and effective approach for monitoring spatial defects in topographic surfaces. The approach improves the representation of surface characteristics through the developed multilabel separation-deviation surface (MSS) model, which labels the important surface characteristics and smooths out the noisy characteristics. We develop two features for monitoring changes in the characteristics of the assigned labels. The MSS feature is introduced for capturing deviations within the assigned labels, and the generalized spatial randomness feature is derived for quantifying deviations between the assigned labels. These two features are integrated into a single monitoring statistic, which is successfully applied for detecting various defects in topographic surfaces, outperforming the traditional monitoring approaches.

We introduce quantum-K ($QK$), a measure of the descriptive complexity of density matrices using classical prefix-free Turing machines and show that the initial segments of weak Solovay random and quantum Schnorr random states are incompressible in the sense of $QK$. Many properties enjoyed by prefix-free Kolmogorov complexity ($K$) have analogous versions for $QK$; notably a counting condition. Several connections between Solovay randomness and $K$, including the Chaitin type characterization of Solovay randomness, carry over to those between weak Solovay randomness and $QK$. We work towards a Levin-Schnorr type characterization of weak Solovay randomness in terms of $QK$. Schnorr randomness has a Levin-Schnorr characterization using $K_C$; a version of $K$ using a computable measure machine, $C$. We similarly define $QK_C$, a version of $QK$. Quantum Schnorr randomness is shown to have a Levin-Schnorr and a Chaitin type characterization using $QK_C$. The latter implies a Chaitin type characterization of classical Schnorr randomness using $K_C$., Comment: 21 pages. This has been submitted to a journal

From the early developments of machines for reasoning and decision making in higher-level information fusion, there was a need for a systematic and reliable evaluation of their performance. Performance evaluation is important for comparison and assessment of alternative solutions to real-world problems. In this paper we focus on one aspect of performance assessment for reasoning under uncertainty: the accuracy of the resulting belief (prediction or estimate). We propose a framework for assessment based on the assumption that the system under investigation is uncertain only due to stochastic variability (randomness), which is partially known. In this context we formulate a distance measure between the “ground truth” and the output of an automated system for reasoning in the framework of one of the non-additive uncertainty formalisms (such as imprecise probability theory, belief function theory or possibility theory). The proposed assessment framework is demonstrated with a simple numerical example.

We investigate the impact of the channel statistics on the average harvested energy when directive lightwave power transfer (DLPT) is used for both indoor and outdoor environments. More specifically, for the indoor scenario, the channel randomness is subject to misalignment effects, while for the outdoor one, atmospheric turbulence is further considered. For both scenarios, closed-form expressions are extracted for the average harvested power, validated via Monte Carlo simulations. Interestingly, the final expressions help to assess the energy harvesting capabilities of a DLPT system and provide valuable insights for hardware implementation.

This paper proposes online algorithms for dynamic matching markets in power distribution systems, which at any real-time operation instance decides about matching -- or delaying the supply of -- flexible loads with available renewable generation with the objective of maximizing the social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for the following generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimal that varies linearly with the variation in the standard deviation. But the same algorithm is shown to not satisfy CO for the second scenario. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimal that varies linearly with the variation in standard deviation plus an offset.

This article presents the construction and consumption of hyperjerk system having chaotic nature with the commensurate ordered fractional derivative on rapidly digitalized emerging technologies. The system analyzed analytically using Lipschitz condition and numerically with the help of predictor corrector approaches. Originality of constructed system is confirmed using log error plots which gives satisfactory small values on finite time scale. The dynamical performances of the complex system are termed in multiple phase planes by the mean of their significant nature. Stability and bifurcation analysis around the fractional derivative is also studies for the various parameter of system to check the visibility of chaotic solution. The system is also designed in analog circuit simulator with the aid of operational amplifier, anti-parallel semiconductor diodes for validation of the system. With the help of random number generators (RNGs), binary array generated from the hyperjerk system and plugged in to the NIST 800–22 test suite to measure the randomness. The array having high randomness is used as strong cypher key for the cryptographic execution straightforwardly in both direction encryption and decryption.

Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion in a heterogeneous social event graph. Specifically, we first construct a heterogeneous graph by adding the hashtag to associate the isolated short texts and describe events comprehensively. Then, we learn view-specific representations of events through graph convolutional networks from the perspectives of text semantics and time distribution, respectively. Finally, we design a hashtag-based multi-view graph attention mechanism to capture the intrinsic interaction across different views and integrate the feature representations to discover events. Extensive experiments on public benchmark datasets demonstrate that MVGAN performs favorably against many state-of-the-art social network event detection algorithms. It also proves that more meaningful signals can contribute to improving the event detection effect in social networks, such as published time and hashtags.

In this paper, we study the stochastic behavior of some lattice beams, called Hencky bar-chain model and their non-local continuous beam approximations. Hencky bar-chain model is a beam lattice composed of rigid segments, connected by some homogeneous rotational elastic links. In the present stochastic analysis, the stiffness of these elastic links is treated as a continuous random variable, with given probability density function. The fundamental eigenfrequency of the linear difference eigenvalue problem is also a random variable in this context. The reliability is defined as the probability that this fundamental frequency is less than an excitation frequency. This reliability function is exactly calculated for the lattice beam in conjunction with various boundary conditions. An exponential distribution is considered for the random stiffness of the elastic links. The stochastic lattice model is then compared to a stochastic nonlocal beam model, based on the continualization of the difference equation of the lattice model. The efficiency of the nonlocal beam model to approximate the lattice beam model is shown in presence of rotational elastic link randomness. We also compare such stochastic function with the one of a continuous local Euler-Bernoulli beam, with a special emphasis on scale effect in presence of randomness. Scale effect is captured both in deterministic and non-deterministic frameworks.

Securing information became essential to exchange multimedia information safely. The exchanged data need to be transformed in a well-managed, secure, and reliable manner. In this paper, we will focus on securing RGB images via cryptography during transmission among users using our effective proposal of utilizing appropriate Pseudo Random Number Generator (PRNG). We implement many techniques of PRNG involved in two consecutive crypto-processes of substitution and transposition to present secure image transformation. Our technique proposal of PRNGs selection is based on testing to encrypt RGB images to be compared with current related used approaches. The work experimentation aims to identify suitability and reliability through security measures standard parameters. The research justifies its proper PRNG selection to model our approach as attractive effective work worth remarking.

We present Helix , a blockchain-based consensus protocol for fair ordering of transactions among nodes in a distributed network. Helix advances in rounds, in each an elected primary node proposes a potential block (a successive set of transactions). For being included in the blockchain, a block must pass validation by an elected committee of nodes. Nodes have two primary preferences. First, to be elected as committee members. Additionally, because each transaction is associated with one of the network nodes, nodes would like to prioritize their own transactions over those of others. Our definition of fairness incorporates three key elements. First, the process of electing nodes to committees is random and unpredictable. Second, a correlated sampling scheme is used to guarantee random selection and ordering of pending transactions in blocks. Third, transactions are encrypted to hide their associations with nodes and prevent censorship. Through the corresponding threshold decryption process we obtain an unpredictable and non-manipulable randomness beacon, which serves both the election process and the correlated sampling scheme. We define a quantitative measure of fairness in the protocol, prove theoretically that fairness manipulation in Helix is significantly limited, and present experiments evaluating fairness in practice.

For the purpose of emulating realistic ray-based channels more exactly, two novel sum-of-cisoids (SOC) channel simulator parameters computation strategies, i.e., the bidirectional allocation (BA) and the simplified forward allocation (FA) methods, are proposed in this letter. Compared with the classical equal power method, the asymmetric computing property of these two methods further improves the spatial correlation emulation accuracy and significantly mitigates the intra-cluster correlation of sub-paths. As shown in the simulation results, both BA and FA methods enable the statistical spatial-temporal characteristics of emulated channel match extremely well with that of the target channel. More important, the proposed processes are deterministic, and will not introduce randomness in different realizations. Hence the proposed methods are always ergodic with arbitrary angle spreads.

A novel data-driven learning control scheme is proposed for unknown systems with unknown fading sensor channels. The fading randomness is modeled by multiplicative and additive random variables subject to certain unknown distributions. In this scheme, we propose an error transmission mode and an iterative gradient estimation method. Unlike the conventional transmission mode where the output is directly transmitted back to the controller, in the error transmission mode, we send the desired reference to the plant such that tracking errors can be calculated locally and then transmitted back through the fading channel. Using the faded tracking error data only, the gradient for updating input is iteratively estimated by a random difference technique along the iteration axis. This gradient acts as the updating term of the control signal; therefore, information on the system and the fading channel is no longer required. The proposed scheme is proved effective in tracking the desired reference under random fading communication environments. Theoretical results are verified by simulations.

This paper presents a simple mathematical model with an electronic circuit for n-th-order hyperjerk system. For demonstration, the proposed iterative methodology comes with a fourth-order parametric control hyperjerk system that relies on the integrator, a summer amplifier, and an adjustable hyperbolic sine nonlinearity function. The proposed hyperjerk system exhibits exceptional unstable behavior at equilibrium. However, the corresponding hidden attractor near to equilibrium point has a high degree of disorder and randomness. The proposed n-th-order chaotic system has some important features such as simple model without multiplication term, simple circuitry, use of n number of capacitors, 2 $$(n-1)$$ number of resistors, and sensitivity with passive components. Moreover, various dynamic behavior and typical time series chaotic responses of the proposed design are validated by incorporating bifurcation sequence, numerical simulation, and practical implementation by using Op-Amp. Also, an application perspective of the proposed hyperjerk system is extended to a random pulse generator (RPG). This concept reflects an idea to obtain the RPG by interfacing with the chaotic signals.

The failure to achieve minimum design overlap between secant piles compromises the ability of a structure to perform as designed, resulting in water leakage or even ground collapse. To establish a more realistic simulation and provide guidelines for designing a safe and cost-effective secant-pile wall, a three-dimensional model of a secant pile, considering the geometric imperfections of the diameter and direction of the borehole, is introduced. An ultrasonic cross-hole test was performed during the construction of secant piles in a launching shaft in Beijing, China. Based on the test results, the statistical characteristics of the pile diameters and orientation parameters were obtained. By taking the pile diameter D, inclination angle β, and azimuth angle α as random variables, Monte Carlo simulations were performed to discuss the influence of different design parameters on the probability density functions of the overlap of secant piles. The obtained results show that the randomness of the inclination angle and pile diameter can be well described by a normal distribution, whereas the azimuth angle is more consistent with a uniform distribution. The integrity of the secant-pile wall can be overestimated without considering the uncertainty of geometric imperfections. The failure of the secant-pile wall increases substantially with increasing spatial variability in drilling inclination and diameter. A design flowchart for pile spacing under the target safety level is proposed to help engineers design a safe and economical pile wall.

Estimation of the scatter of the durability at the second stage of fatigue, namely at the stage of crack propagation is a problem of scientific and obvious practical importance: machines operate according to their technical condition, which means monitoring of the actual crack length during their service life. The limits of the spread of the strength values at the stage of crack propagation in aluminum samples are studied using published data and a previously developed model. In view of the great importance of this problem, a special simulation model was used to generate the extrema of a random sequence based on target Markov matrices. On the one hand, this simulation method guarantees the characteristic traits of real sequence in exploitation (TWIST standard in this example). On the other hand, it contains reasonable randomness — these two parts together provide an opportunity to study the variability of the crack growth rate. For the simulation experiment, literature data on aluminum and steel samples were used along with popular fatigue crack growth models (Paris, Foreman and Willenborg models). In addition, the Miner’s summation rule was quantitatively tested to estimate the crack growth resistance coefficient under various loads. The agreement with the literature experimental data is shown. Preliminary data on the effect of the type of loading (random or block) on the durability are given on the basis of scientific literature data. The proposed simulation method can be useful for testing various models. It is also intended to develop an experimental design for laboratory testing in the future.

In cryptosystems, the generation of random keys is crucial. The random number generator is required to have a sufficiently fast generation speed to ensure the size of the keyspace. At the same time, the randomness of the key is an important indicator to ensure the security of the encryption system. The chaotic random number generator has been widely used in cryptosystems due to the uncertainty, non-repeatability, and unpredictability of chaotic systems. However, chaotic systems, especially high-dimensional chaotic systems, have slow calculation speed and long iteration time. This caused a conflict between the number of random keys and the speed of generation. In this paper, we introduce the Least Squares Generative Adversarial Networks(LSGAN)into random number generation. Using LSGAN’s powerful learning ability, a novel learning random number generator is constructed. Six chaotic systems with different structures and different dimensions are used as training sets to realize the rapid and efficient generation of random numbers. Experimental results prove that the encryption key generated by this scheme can pass all randomness tests of the National Institute of Standards and Technology (NIST). Hence, our result shows that LSGAN has the potential to improve the quality of the random number generators. Finally, the results are successfully applied to the image encryption scheme based on selective scrambling and overlay diffusion, and good results are achieved.

The majority of existing image encryption algorithms use single chaotic systems, such as Logistic map or Lorentz system, to be the pseudo-random sequence generator. Actually, neither low nor high dimensional chaotic system can get rid of pseudo-randomness deterioration of chaotic sequence calculated by limited precision computer. Additionally, most of them use only one chaotic pseudo-random sequence throughout the encryption process. These are the more obvious deficiencies. In the current paper, a novel compound chaotic system is applied to color images domain to solve the mentioned problems. Dynamic parameter-control chaotic system can enhance the sequence’s randomness after digitalizing. Corresponding the different sequences generated by the novel chaotic system to each color channel of image is a helpful method to reduce the image’s statistical characteristics in the scrambling process. Finally, the effectiveness and security of the proposed encryption has been illustrated by the experimental, and at the meantime, excellent performance is also demonstrated.

To investigate the dynamic responses and comprehending the damage mechanism of coral aggregate concrete (CAC) in compression, both the experimental and numerical investigations were implemented in the present work. Firstly, the dynamic mechanical properties of CAC at different strain rates were tested through the Split-Hopkinson pressure bar (SHPB) tests. Moreover, the effects of concrete strength grade and strain rate on CAC were discussed and analyzed. Subsequently, we developed the three-dimensional (3D) random mesoscale model considering the randomness of aggregate shape, size and distribution at meso-level, which was validated and employed in the numerical simulation of CAC in compression. The results indicate that the splitting failure passing through the coral aggregate is CAC’S primary failure mode. It has been found that the failure pattern, deformation process, and dynamic increasing factor of CAC are associated with both the strain rate and concrete strength grade. Furthermore, by comparing the experimental and mesoscopic results, it has been proven to be reliable to employ the developed 3D mesoscale modelling method to simulate CAC’s dynamic performances, which has enormous potential in future research of CAC under intense dynamic loadings.

Negative selection algorithm is the core algorithm of artificial immune system. It only uses the self for training and generates detectors to detect abnormalities. Holes are feature space areas that the detector fails to cover, it is the root cause of the performance degradation of the negative selection algorithm. The conventional method generates a large number of detectors randomly to repair the holes, which is time-consuming and not effective. To alleviate the problem, we propose a V-Detector-KN algorithm in this paper. V-Detector is the abbreviation of the real-valued negative selection algorithm with Variable-sized Detectors, KN represents Known Nonself. The V-Detector-KN algorithm uses the known nonself as the candidate detector to further generate the detector based on the V-Detector randomly generated detector, so as to realize the repair of holes. Compared with the conventional method to randomly generate detectors to repair holes, our proposed V-Detector-KN method uses known nonself to repair holes, reducing the randomness and blindness of hole repair. Theoretical analysis shows that the detection rate of our algorithm is not lower than that of the conventional V-Detector algorithm. The results of experiment comparing with other 6 algorithms on 7 UCI data sets show the superiority of our proposed algorithm.

With the ease of deployment, capabilities of evading the jammers and obscuring their existence, unmanned aerial vehicles (UAVs) are one of the most suitable candidates to perform surveillance. There exists a body of literature in which the inspectors follow a deterministic trajectory to conduct surveillance, which results in a predictable environment for malicious entities. Thus, introducing randomness to the surveillance is of particular interest. In this work, we propose a novel framework for stochastic UAV-assisted surveillance that i) inherently considers the battery constraints of the UAVs, ii) proposes random moving patterns modeled via random walks, and iii) adds another degree of randomness to the system via considering probabilistic inspections. We formulate the problem of interest, i.e., obtaining the energy-efficient random walk and inspection policies of the UAVs subject to probabilistic constraints on inspection criteria of the sites and battery consumption of the UAVs, which turns out to be signomial programming that is highly non-convex. To solve it, we propose a centralized and a distributed algorithm along with their performance guarantee. This work contributes to both UAV-assisted surveillance and classic random walk literature by designing random walks with random inspection policies on weighted graphs with energy limited random walkers., This paper is accepted for publication in IEEE Transactions on Wireless Communications

We provide a new tool for simulation of a random variable (target source) from a randomness source with side information. Considering the total variation distance as the measure of precision, this tool offers an upper bound for the precision of simulation, which is vanishing exponentially in the difference of Rényi entropies of the randomness and target sources. This tool finds application in games in which the players wish to generate their actions (target source) as a function of a randomness source such that they are almost independent of the observations of the opponent (side information). In particular, we study zero-sum repeated games in which the players are restricted to strategies that require only a limited amount of randomness. Let be the max-min value of the n stage game. Previous works have characterized [Formula: see text], that is, the long-run max-min value, but they have not provided any result on the value of v n for a given finite n-stage game. Here, we utilize our new tool to study how v n converges to the long-run max-min value.

We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of their main problems is visual clutter which results from overlap. To reduce this overlap, jitter plots were introduced, whereby an additional, non-encoding plot dimension is introduced, along which the data point representing dots are randomly perturbed. Unfortunately, this randomness can suggest non-existent clusters, and often leads to visually unappealing plots, in which overlap might still occur. To overcome these shortcomings, we introduce BlueNoise Plots where random jitter along the non-encoding plot dimension is replaced by optimizing all dots to keep a minimum distance in 2D i. e., Blue Noise. We evaluate the effectiveness as well as the aesthetics of Blue Noise Plots through both, a quantitative and a qualitative user study. The Python implementation of Blue Noise Plots is available here.

The reconfigurable intelligent surface (RIS) is envisioned to create ultra-secure wireless networks. Previous works on the RIS-assisted security provisioning techniques assumed wiretap channel information at the transmitter, which is practically unavailable in a passive eavesdropping scenario. In this article, we consider a point-to-point anti-eavesdropping system in which a RIS is used to enable the secure transmission from a multi-antenna transmitter to a multi-antenna legitimate receiver. A passive eavesdropper, whose channel state information is completely unknown, attempts to decode the secret messages. We propose a security approach by using the reflection at the RIS as multiplicative randomness against the wiretapper. Specifically, the reflection coefficients in terms of amplitude and phase are updated in each transmission and kept private at the RIS. Through the reflection designs, the effective channel matrix is diagonalized at the legitimate receiver. In contrast, the eavesdropper receives coupled signals with the weights of the randomness at RIS. The main contributions of this article are three reflection designs and correspondingly three secure transmission schemes, which fulfills diverse requirements of the balance amongst performance metrics including the degrees of randomness, spectral efficiency, and reliability. The main benefits of the proposed secure transmission schemes are four-fold. First, the transmitter does not need to know the eavesdropper’s channel states. Second, closed-form solutions for the reflection coefficients are provided. Third, the legitimate receiver has a linear decoding complexity. Fourth, the unauthorized wiretapper is unable to cancel out the multiplicative randomness and thus cannot extract much useful information. Numerical results show that exploiting the RIS as a source of multiplicative randomness provides a new perspective to improve the security of the wireless networks.

Theoretically, any chaotic system or chaotic map has ideal complex dynamics. However, because of the finite precision of simulation software and digital devices during implementation, chaotic systems often undergo dynamical degradation, which hinders the further application of digital chaotic systems in many fields. Therefore in this paper, the method based on the perturbation and Unscented Kalman Filter (UKF) theory is designed to counteract the dynamical degradation of digital chaotic systems. Specifically, the UKF algorithm is employed to reinstate the original dynamic performance of the chaotic system, and then perturbation feedback technology is used to cause the chaotic system to obtain strong dynamic performance to resist attacks. The experimental and simulation results demonstrate that this method has good effect on improving the dynamic degradation of digital chaotic map. In addition, the corresponding pseudorandom number generator (PRNG) is constructed via this method, and its randomness is evaluated using the National Institute of Standards and Technology (NIST) SP800-22 and TestU01 test suites. By comparing with other schemes, it can be seen that this PRNG has better performance which illustrates the proposed scheme can be applied in the chaos-based cryptography and utilized in other potential applications.

Randomness and fuzziness are two fundamental kinds of uncertain phenomena, which can be handled respectively by probability theory (sigma system) and possibility theory (max system). For many practical problems of information processing, random uncertainty may often co-exist with fuzzy uncertainty, which reminds us to look for a novel mechanism called sigma-max hybrid uncertainty inference. This mechanism can cope with randomness and fuzziness jointly and achieve a direct fusion of heterogeneous information modeled by probability or possibility. Specifically, we are interested in two typical forms of uncertainty inference, i.e., uncertainty update equation combining heterogeneous information as well as composition rule of heterogeneous relations. Such an objective is achieved by the adoption of joint description of a random variable and a fuzzy variable, i.e., hybrid distribution of probability and possibility, which lays the foundation for connecting the two uncertainty systems of probability and possibility, and results in the integrated sigma-max system. The hybrid distribution, other than joint probability (or possibility) distribution, has a couple of special normalization requirements that are order-dependent with respective to “addition” operation over random variable and “max’ operation over fuzzy variable. The derived sigma-max inference is applied to target recognition, an example of which is simulated to demonstrate the merit of the proposed method over the classic Bayesian classifier when randomness and fuzziness are both involved.

Public transport is affected by different types of randomness which impact the reliability they offer. The source of this randomness may come, for example, from casual uncertainty sources, such as accidents or protests, or from systematic uncertainty, related to service supply, such as dwelling time or travel time between consecutive stops. In addition to these sources of variability, an extra source of uncertainty experienced by users arises due to the public transport vehicle not being immediately available to avoid a wait of uncertain length. In a frequency-based system (without schedules), even under perfect regularity, users are not sure about the next vehicle's time of arrival. This article aims to find out the impact that reliability has on travellers’ public transport alternative choice. To do so, an experiment of stated preferences is carried out, where the design characteristics are four operational attributes: speed, frequency, headway regularity, and average demand. Every scenario is randomly generated, based on the operational characteristics of the specific scenario. This means the alternatives presented resemble real-life operation and they are different between respondents. A Hybrid Discrete Choice Model was estimated, which addresses preference heterogeneity by considering two latent attitudes: punctual behaviour and crowding aversion. Overall, results indicate that headway irregularity has a significant effect on travellers’ choices, both in terms of waiting time and passenger density. This confirms this attribute should not be ignored in any public transport model, especially when it comes to evaluate projects which improve the system’s reliability but not necessary its average level of service.

In this paper, we propose an ultra-high speed random bit generator without the time-delay signature based on an ytterbium-doped random fiber laser (YRFL) with Rayleigh scattering feedback. The spectrum of the YRFL has a relatively broad bandwidth (0.35 nm) and the lasing temporal intensity shows random fluctuations without cavity induced time-delay signatures (TDS), which are essential for ultra-high speed random bit generation. The chaotic signal and its time-delayed signal sampling at 40 Giga samples per second (GS/s) are converted to digital 8-bit signals. By selecting 5 least significant bits from each 8-bit digital signal and using bitwise exclusive-OR operation, we experimentally achieve 200 Gbps physical random bit generation based on ytterbium-doped random fiber laser with the verified randomness. The combination of broadband emission and free of TDS makes random fiber lasers new promising sources for high performance random bit generation in a simple and compact configuration, which has a great potential in cryptography and secure communication applications.

We investigate the problem of almost zeros of polynomial matrices as used in system theory. It is related to the controllability and observability notion of systems as well as the determination of Macmillan degree and complexity of systems. We also present some new results on this important invariant in the light of randomness and we prove an uncertainty type relation appearing in such ensembles of operators.

In this work, an effective classification of hyperspectral images is modelled and simulated with the proximal support vector machine (PSVM) by integrating them with the deep learning approach. The modelled new deep proximal support vector machines are designed in a manner to handle the existing complexity, discrepancies and irregularities in the traditional hyperspectral image classifiers. This paper investigates the applicability of the new deep linear and nonlinear proximal support vector machines as applied for hyperspectral image classification. In respect of the new deep PSVM classifier, it is modelled for deep linear PSVM and deep nonlinear PSVM to perform classification of spectral images so as to bring out the best classifier model. To test and validate the proposed deep PSVM classifiers University of Pavia datasets, Indian Pine datasets and Kennedy Space Centre datasets are employed as test beds and results are attained. The developed new deep PSVM classifiers are developed with varied kernel functions to do the classification process. The deep learning technique enhances the linear and nonlinear PSVM classifier models to perform more effectively during the learning process and carry out the classification using auto-encoders and decoders. Results attained during the process infer that the developed new deep PSVM (linear and nonlinear) has come out with better classification accuracy in comparison with that of the other techniques from literature for the same datasets. Statistical analysis validates the randomness that occurs in the proposed deep learning techniques as applied for spectral image classification.

This work presents a framework to study the quasi-static brittle fracture in functionally graded materials with random material properties using a non-intrusive phase field method. The model accounts for variation in the material and fracture properties. Further, the effective Young’s modulus, the gradient index and the fracture properties of the constituent materials are considered as random input variables. The output randomness is represented by polynomial chaos expansion. Two-dimensional benchmark numerical examples are presented to demonstrate the benefits of the non-intrusive approach to study fracture characteristics. The influence of the randomness in the material properties on the peak load carrying capacity is considered as a primary quantity of interest in this study. A systematic parametric study is carried out to bring out the sensitivity of the input randomness on the stochastic output response using Sobol indices.

Purpose The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem. Design/methodology/approach First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency. Findings The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method. Originality/value The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.

Encryption technology has a great influence on data security. There are many encryptions have been proposed. In general, the encryptions with higher complexity will provide higher security, but it will also consume a lot of computing resources. In some cases, these high complexity method may not suitable such as Internet of Thing devices. This is because Internet of Thing device has lower computing power due to the size and the limited battery. Therefore, most of Internet of Thing (IoT) devices are used symmetric encryption as the main method. However, this kind of symmetric encryption algorithms are easy to occur weak keys such as data encryption algorithm (DES). This will expose IoT devices to high risk environments. In this study, a new fitness function has given and then a Genetic Algorithm-based symmetric key generation is proposed. As the simulation results, our proposed method can provide higher randomness and very low probability to occur the weak keys so that the data security in IoT environment will be increased significantly.