Discrete Input Research Articles

Model Predictive Control for Neuromimetic Quantized Systems

Based on our recent research on neural heuristic quantized systems, we propose an emulation problem consistent with our recently introduced neuromimetic paradigm. This optimal quantization problem can be solved with model predictive control (MPC) by deriving the conditions under which the quantized system can simultaneously guarantee (asymptotic) stability and emulation given dynamical system by optimizing a Lyapunov-like objective function. Taking inspiration from neurobiology, the neuromimetic model features large numbers of discrete inputs that collectively produce stable motions that emulate the behavior of a continuous system. The emulation is produced by solving an optimization problem involving integer variables. The approach in the paper begins by performing the optimization using model predictive control (MPC) and then using a neural network to train a model using the data generated in this process. Complexity is reduced by applying Fincke and Pohst's sphere decoding algorithm to narrow down the search for the optimal solution.

Dynamic emergence of relational structure network in human brains.

Reasoning the hidden relational structure from sequences of events is a crucial ability humans possess, which helps them to predict the future and make inferences. Besides simple statistical properties, humans also excel in learning more complex relational networks. Several brain regions are engaged in the process, yet the time-resolved neural implementation of relational structure learning and its contribution to behavior remains unknown. Here human subjects performed a probabilistic sequential prediction task on image sequences generated from a transition graph-like network, with their brain activities recorded using electroencephalography (EEG). We demonstrate the emergence of two key aspects of relational knowledge - lower-order transition probability and higher-order community structure, which arise around 540-930ms after image onset and well predict behavioral performance. Furthermore, computational modeling suggests that the formed higher-order community structure, i.e., compressed clusters in the network, could be well characterized by a successor representation operation. Overall, human brains are computing the temporal statistical relationship among discrete inputs, based on which new abstract graph-like knowledge could be constructed.

Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful

Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model. We generalize stacking to Bayesian hierarchical stacking. The model weights are varying as a function of data, partially-pooled, and inferred using Bayesian inference. We further incorporate discrete and continuous inputs, other structured priors, and time series and longitudinal data. To verify the performance gain of the proposed method, we derive theory bounds, and demonstrate on several applied problems.

Pruning Decision Rules by Reduct-Based Weighting and Ranking of Features.

Methods and techniques of feature selection support expert domain knowledge in the search for attributes, which are the most important for a task. These approaches can also be used in the process of closer tailoring of the obtained solutions when dimensionality reduction is aimed not only at variables but also at learners. The paper reports on research where attribute rankings were employed to filter induced decision rules. The rankings were constructed through the proposed weighting factor based on the concept of decision reducts-a feature reduction mechanism embedded in the rough set theory. Classical rough sets operate only in discrete input space by indiscernibility relation. Replacing it with dominance enables processing real-valued data. Decision reducts were found for both numeric and discrete attributes, transformed by selected discretisation approaches. The calculated ranking scores were used to control the selection of decision rules. The performance of the resulting rule classifiers was observed for the entire range of rejected variables, for decision rules with conditions on continuous values, discretised conditions, and also inferred from discrete data. The predictive powers were analysed and compared to detect existing trends. The experiments show that for all variants of the rule sets, not only was dimensionality reduction possible, but also predictions were improved, which validated the proposed methodology.

Magnetic Skin for Touchless Human–Computer Interaction to Prevent Healthcare‐Associated Infections

AbstractHealthcare‐associated infections are a major problem in hospitals, and regular disinfection methods cannot guarantee that all pathogens are eliminated. Non‐contact human–computer interactions can effectively reduce the risk of contamination by preventing direct contact between medical staff and electronic products. In this study, a new type of flexible and highly malleable magnetic skin composed of a mixture of an elastic matrix and permanent magnetic powder is demonstrated. In addition, the relationship between the magnetic skin properties and the elastic matrix, magnetic powder material, and their ratio is studied. In addition, the application of magnetic skin in gesture recognition (representing discrete gesture input) and magnetic positioning (representing continuous input) is explored to easily achieve touchless human–computer interactions. Both perform well and provide a new way for future human–computer interaction.

Selection of Appropriate Symbolic Regression Models Using Statistical and Dynamic System Criteria: Example of Waste Gasification

In this paper, we analyze the interpretable models from real gasification datasets of the project “Centre for Energy and Environmental Technologies” (CEET) discovered by symbolic regression. To evaluate CEET models based on input data, two different statistical metrics to quantify their accuracy are usually used: Mean Square Error (MSE) and the Pearson Correlation Coefficient (PCC). However, if the testing points and the points used to construct the models are not chosen randomly from the continuum of the input variable, but instead from the limited number of discrete input points, the behavior of the model between such points very possibly will not fit well the physical essence of the modelled phenomenon. For example, the developed model can have unexpected oscillatory tendencies between the used points, while the usually used statistical metrics cannot detect these anomalies. However, using dynamic system criteria in addition to statistical metrics, such suspicious models that do fit well-expected behavior can be automatically detected and abandoned. This communication will show the universal method based on dynamic system criteria which can detect suitable models among all those which have good properties following statistical metrics. The dynamic system criteria measure the complexity of the candidate models using approximate and sample entropy. The examples are given for waste gasification where the output data (percentage of each particular gas in the produced mixture) is given only for six values of the input data (temperature in the chamber in which the process takes place). In such cases instead, to produce expected simple spline-like curves, artificial intelligence tools can produce inappropriate oscillatory curves with sharp picks due to the known tendency of symbolic regression to produce overfitted and relatively more complex models if the nature of the physical model is simple.

ПАРАМЕТРИЧЕСКОЕ СОГЛАСОВАНИЕ УСТРОЙСТВ КОНТРОЛЯ ИЗОЛЯЦИИ И СОПТ

The aim of the study is to develop a method for parametric matching of insulation monitoringdevices, unearthed DC distribution systems and discrete inputs of relay protection.The use of unearthed DC distribution systems for power supply to relay protection devicesand other responsible consumers makes it possible to ensure high reliability and safety during their operation. It is necessary to ensure uninterrupted power supply to these consumerseven in the event of a ground fault in one of the DC distribution network poles. Insulationmonitoring of the network poles and the prompt execution of repair work when the insulationresistance falls below the critical level set by the response value, are a guarantee of highreliability of power supply. The article discusses the reasons leading to dangerous levels ofpole voltage unbalance and false triggering of relay protection discrete inputs during groundfaults of the signal lines connected to them. A method based on multivariate DC analysis isproposed to determine the matched insulation monitoring response value with the DC networkparameters and discrete inputs parameters, when the conditions for the occurrence ofsuch accidents are excluded. The influence of insulation monitoring devices in a DC systemon pole voltages unbalance and relay protection operation is also considered. A modificationof the insulation monitoring active method is proposed, which does not create additional polevoltage unbalance and risks of relay protection misoperation. The operating range of thestatic characteristics of the pole voltages and leakage current measuring transducers is narrowedin the grid unbalanced modes by using a modified active insulation monitoring met hod.Estimates of the increase in the insulation resistance measurement error arising in thesemodes are obtained.

Stationary scattering solution and logic operations in X-coupled tight-binding chains

The realization of logical operations on X-shaped structures consisting of two discrete input chains, which are linearly connected to two output chains through a central coupling region, is investigated. We provide the exact stationary scattering solution for the harmonic waves propagating in the proposed structure and report the full transmission spectrum. We show that a number of Boolean logic operations can be implemented using a digitization scheme of the output signal based on the amplitude modulation of the transmission coefficients. In particular, we show that the relative phase between input harmonics can be used as a control parameter to select the desired logic operation. A detailed study of the contrast ratio is used to identify optimal regions in the parameter space for the operation of each logical function.

Deep learning based analysis of microstructured materials for thermal radiation control

Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with varying material, wavelength, and temperature conditions relevant to thermal radiation, the microstructure design optimization becomes a very time-intensive process and with results for specific and limited conditions. Here, we develop a deep neural network to emulate the outputs of finite-difference time-domain simulations (FDTD). The network we show is the foundation of a machine learning based approach to microstructure design optimization for thermal radiation control. Our neural network differentiates materials using discrete inputs derived from the materials’ complex refractive index, enabling the model to build relationships between the microtexture’s geometry, wavelength, and material. Thus, material selection does not constrain our network and it is capable of accurately extrapolating optical properties for microstructures of materials not included in the training process. Our surrogate deep neural network can synthetically simulate over 1,000,000 distinct combinations of geometry, wavelength, temperature, and material in less than a minute, representing a speed increase of over 8 orders of magnitude compared to typical FDTD simulations. This speed enables us to perform sweeping thermal-optical optimizations rapidly to design advanced passive cooling or heating systems. The deep learning-based approach enables complex thermal and optical studies that would be impossible with conventional simulations and our network design can be used to effectively replace optical simulations for other microstructures.

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

An adaptive subset simulation algorithm for system reliability analysis with discontinuous limit states

Many system reliability problems involve performance functions with a discontinuous distribution. Such situations occur in both connectivity- and flow-based network reliability problems, due to binary or multi-state random variables entering the definition of the system performance or due to the discontinuous nature of the system model. When solving this kind of problems, the standard subset simulation algorithm with fixed intermediate conditional probability and fixed number of samples per level can lead to substantial errors, since the discontinuity of the output can result in an ambiguous definition of the sought percentile of the samples and, hence, of the intermediate domains. In this paper, we propose an adaptive subset simulation algorithm to determine the reliability of systems whose performance function is a discontinuous random variable. The proposed algorithm chooses the number of samples and the intermediate conditional probabilities adaptively. We discuss two MCMC algorithms for generation of the samples in the intermediate domains, the adaptive conditional sampling method and a novel independent Metropolis–Hastings algorithm that efficiently samples in discrete input spaces. The accuracy and efficiency of the proposed algorithm are demonstrated by a set of numerical examples.

Nearest neighbor control for practical stabilization of passive nonlinear systems

This paper studies static output feedback stabilization of continuous-time (incrementally) passive nonlinear systems where the control actions can only be chosen from a discrete (and possibly finite) set of points. For this purpose, we are working under the assumption that the system under consideration is large-time norm observable and the convex hull of the realizable control actions contains the target constant input (which corresponds to the equilibrium point) in its interior. We propose a nearest-neighbor based static feedback mapping from the output space to the finite set of control actions, that is able to practically stabilize the closed-loop systems. Consequently, we show that for such systems with m-dimensional input space, it is sufficient to have m+1 discrete input points (other than zero for general passive systems or the target constant input for constant-incrementally passive systems). Furthermore, we present a constructive algorithm to design such m+1 nonzero input points that satisfy the conditions for practical stability using our proposed nearest-neighbor control.

The quantitative application of channel importance in movement intention decoding

The complex brain network consists of multiple collaborative regions, which can be activated to varying degrees by motor imagery (MI) and the induced electroencephalogram (EEG) recorded by an array of scalp electrodes is usually decoded for driving rehabilitation system. Either all channels or partially selected channels are equally applied to recognize movement intention, which may be incompatible with the individual differences of channels from different locations. In this paper, a channel importance based imaging method is proposed, denoted as CIBI. For each electrode of MI-EEG, the power over 8–30 Hz band is calculated from discrete Fourier spectrum and input to random forest algorithm (RF) to quantify its contribution, namely channel importance (CI); Then, CI is used for weighting the powers of α and β rhythms, which are interpolated to a 32×32 grid by using Clough-Tocher method respectively, generating two main band images with time-frequency-space information. In addition, a dual branch fusion convolutional neural network (DBFCNN) is developed to match with the characteristic of two MI images, realizing the extraction, fusion and classification of comprehensive features. Extensive experiments are conducted based on two public datasets with four classes of MI-EEG, the relatively higher average accuracies are obtained, and the improvements achieve 23.95% and 25.14% respectively when using channel importance, their statistical analysis are also performed by Kappa value, confusion matrix and receiver operating characteristic. Experiment results show that the personalized channel importance is helpful to enhance inter-class separability as well as the proposed method has the outstanding decoding ability for multiple MI tasks.

Reconstruction of propeller deformation based on FBG sensor network

Real-time prediction of the deformed propeller has important implications in engineering applications. While, the traditional contact measurement method cannot satisfy the on-line measurement of propeller deformation, because the weight and arrangement of the strain gage used will make it difficult to measure propeller deformation. In this paper, the FBG sensing technology was applied to detect propeller deformation, which was constructed based on discrete input strain measures in the structure. To avoid the inaccuracy and complexity of contact measurement of propeller deformation, this paper proposed a strain-deformation reconstruction method (inverse finite element method based), where a linear discretization-inverse finite element deformation reconstruction algorithm was developed based on the unique shape and structure of the propeller at the same time. In this paper, a propeller strain-deformation reconstruction experimental system based on the FBG sensor network is constructed, according to which a single propeller blade is loaded and the deformation is reconstructed. Meanwhile, the deformation of the blade is measured with a Hexagon Three-Coordinate Measuring Arm. From the study, the error between the reconstructed deformation and the measured deformation propeller is within 7% under the concentrated load of 50N, 100N and 150N, which clearly verifies the feasibility and accuracy of the strain-deformation reconstruction of the propeller.

A metric for quantifying nonlinearity in k-dimensional complex-valued functions

Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k-dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.

A probability based methodology for multi object optimization in material selection

Multi object optimization in material selection involves the satisfaction of optimizing the multi attributes simultaneously, which analogically corresponds to the simultaneous appearance of the event of the multi attributes in the viewpoint of probability theory, thus the optimization of multi – object becomes the assessment of the “joint probability” of these multi – attribute problem. Furthermore, the preferential degree of the candidate material in the material selection is reflected by the concept of preferential probability, and a quantitative approach for evaluating the partial preferential probability of each material attribute indicator and the total (joint) preferential probability of candidate material in the material selection is proposed on basis of probability theory correspondingly. In the approach, all material attribute indicators are divided into beneficial or unbeneficial types; each material attribute indicator of the candidate contributes one partial preferential probability linearly to its authorized material upon its nature of whether beneficial or unbeneficial type merely; the product of all partial preferential probabilities of a candidate makes its total preferential probability, which is the final unique index in the material selection decisively; the candidate materials can be ranked according to their total preferential probabilities, which determines the result of the selection. Furthermore, the condition of discrete input variables and the objects is extended to the case of continuous input variables and the objects. Some examples are given in detail, satisfied results are obtained.

A hierarchical decomposition of internal wave fields

Internal gravity wave fields are decomposed into temporal modes revealing the hierarchical structure of nonlinear wave–wave interactions. We present a novel fusion of Green's functions for solving the forced internal wave equation with a weakly nonlinear perturbation expansion. Our approach is semi-analytical, based on integration over finite elements with the perturbation expansion ensuring source terms at each order are only dependent on the solutions at lower orders. Thus, the procedure is purely inductive and efficient to compute. To perform a thorough validation of our new method, we diagnose experiments using synthetic Schlieren and apply sophisticated post-processing techniques, including dynamic mode decomposition, to obtain these temporal modes for systems with discrete input frequencies. By decomposing the experimental field and comparing individual constituents against equivalents synthesised by our model, we are able to present the first truly comprehensive, validated, mechanistic picture of wave–wave interactions to arbitrary order. This synergy enables us to identify non-wave oscillatory behaviour at frequencies shared by waves in the hierarchy and leads us to discover an important open question regarding transmission efficiency within individual wave–wave interactions. Although our experiments are generated by boundary displacements, we present equivalences between source terms and boundary displacements so that the class of applicable systems may be broadened. Our technique also generalises to aperiodic and unbounded configurations and to any weakly nonlinear wave-governed system for which there is an available Green's function.

BO-B&B: A hybrid algorithm based on Bayesian optimization and branch-and-bound for discrete network design problems

<abstract> <p>A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&B can potentially aid decision makers in mapping practical network design schemes for large-scale networks.</p> </abstract>

Optimal Probabilistic Constellation Shaping for Covert Communications

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose non-equiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

РАЗРАБОТКА АЛГОРИТМА ИНТЕРПРЕТАЦИИ ВХОДНЫХ ПЕРЕМЕННЫХ НЕЧЕТКОГО ЛОГИЧЕСКОГО КОНТРОЛЛЕРА ДЛЯ СИСТЕМ РЕАЛЬНОГО ВРЕМЕНИ

The purpose of the research work is to develop an algorithm for interpreting input variables by a discrete membership function of a fuzzy set for embedded and real-time systems. To achieve the goal set in the scientific work, the following tasks were solved: the study of fuzzy logical inference in automatic control systems: analysis of the fuzzy set building, representation of a discrete input values; development of an algorithm and software implementation for real-time systems. Methods for setting input variables for fuzzy logic systems were considered, an algorithmic implementation of the representation of the input value as an element of a fuzzy set and automatic calculation of the membership function was proposed.