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This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

Conventional design and optimization of passive compensation network (PCN) for inductive power transfer (IPT) system are based on specific topologies. The demerits of this design method are: i) The topology is mostly chosen by experience; ii) The design parameters are not multi-objective optimal. Aiming at these issues, this paper proposes an optimal PCN design scheme based on evolutionary algorithm (EA) to synchronously optimize the topology and parameters of PCN for IPT system. Firstly, a unified mathematical model of the PCN is presented and derived by transmission matrix. Then, according to the mathematical model, the multi-objective functions (such as output fluctuation and efficiency) as well as the constraints (such as load and coupling coefficient) for the optimal PCN design are established. The EA based multi-objective optimal PCN design algorithm is further constructed. Six optimal results are obtained using the algorithm, and one optimized PCN having minimum output current fluctuation and high-efficiency is chosen to validate the effectiveness of the proposed design scheme in experiment. For the given IPT system with the optimized PCN, the maximum fluctuation of output current is no more than 11%, within 200% of load variation and about 77% of coupling variation. [ABSTRACT FROM AUTHOR]

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided. [ABSTRACT FROM AUTHOR]

The convergence of the waveform relaxation (WR) method is demonstrated for a class of circuits: Chains of identical and symmetrical passive subcircuits. The WR algorithm uses resistive coupling to implement the iteration. Every part is modeled as a symmetric and reciprocal linear two-port network. The iteration matrices of the WR operator are constructed for the Gauss-Jacobi and Gauss-Seidel relaxations in the Fourier domain. An upperbound estimate of the spectral radius of the WR operator is presented. It demonstrates the convergence of the method independently of the number of cascaded parts in the chain and the coupling resistance. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS-type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS-type solvers used for linear, nonlinear, and hybrid MPC. [ABSTRACT FROM PUBLISHER]

Purpose In this paper, a new structure for the permanent magnet hysteresis synchronous (PMHS) motor is introduced. Moreover, this paper aims to presents a new analytical method for modeling of a disc-type PMHS motor.Design/methodology/approach Because the hysteresis and permanent magnet motors have unique characteristics, a motor (the PMHS motor) with excellent performance features can be achieved by combining them. Choosing a disc-type slotless structure causes the major advantages of both motors to be preserved in the new motor. To analyze PMHS motor, mathematical equations are obtained by using Ampere’s circuital law, flux continuity law and Faraday’s law. Then the air-gap voltage and exciting current of the motor can be calculated. To implement this method, a new iterative algorithm is proposed. This algorithm consists of one-iteration loop for each input voltage to find the maximum flux density of the operational hysteresis loop of the motor.Findings Validity of the analytical approach is confirmed by experimental results. A reasonably close agreement between the two is shown and some outstanding performances of the PMHS motor are demonstrated.Originality/value A new structure for PMHS motor and also a new analytical method for performance prediction of this motor is presented. [ABSTRACT FROM AUTHOR]

The article discusses problems with efforts to develop algorithms for finding a Nash equilibrium as researched by computer scientists. Topics include the relation of Nash equilibria to game theory, the need for better models to be used by economists modeling markets, and comments from researchers including Christos Papadimitriou, Aviad Rubinstein, and Karthik C.S.

Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

A report from Xuzhou University of Technology in China discusses the use of mathematical models to predict the impact of regional return to work after an epidemic. The researchers propose a multistage mixed integer programming model for humanitarian logistics and transport resources, specifically focusing on the distribution and delivery of medicine resources in a cost-effective and efficient manner. The proposed model is solved using a Benders decomposition algorithm, and two case studies are used to validate the model and algorithm. The research concludes that the model and algorithm are effective in solving the dynamic medicine demand information in humanitarian relief situations. [Extracted from the article]

This paper considers the problem of a leader that seeks to optimally influence the opinions of agents in a directed network through connecting with a limited number of the agents (“direct followers”), possibly in the presence of a fixed competing leader. The settings involving a single leader and two competing leaders are unified into a general combinatoric optimization problem, for which two heuristic approaches are developed. The first approach is based on a convex relaxation scheme, possibly in combination with the $\ell _1$ -norm regularization technique, and the second is based on a greedy selection strategy. The main technical novelties of this work are in the establishment of supermodularity of the objective function and convexity of its continuous relaxation. The greedy approach is guaranteed to have a lower bound on the approximation ratio sharper than $(1-1/e)$ , while the convex approach can benefit from efficient (customized) numerical solvers to have practically comparable solutions possibly with faster computation times. The two approaches can be combined to provide improved results. In numerical examples, the approximation ratio can be made to reach ${\text{90}\%}$ or higher depending on the number of direct followers. [ABSTRACT FROM AUTHOR]

Improvements to the exhaustive search method of best-match file searching have previously been achieved by doing a preprocessing step involving the calculation of distances from a reference point. This paper discusses the proper choice of reference points and extends the previous algorithm to use more than one reference point. It is shown that reference points should be located outside of data clusters. The results of computer simulations are presented which show that large improvements can be achieved by the proper choice and location of multiple reference points. [ABSTRACT FROM AUTHOR]

The use of decision tables as a tool in systems analysis and for program specification is now becoming accepted. Rules on redundancy, contradiction, and completeness for limited entry tables were published in 1963. These ore usually used for checking, preceded if necessary by a conversion from extended to limited entry form. Processors which automatically translate tables to more conventional program usually base their diagnostic facilities on these rules. In this paper it is suggested that these rules ore unsatisfactory and that the important aspect of checking is to eliminate ambiguity from tables. Ambiguity is defined and discussed, and a procedure for producing checked out decision tables is proposed. The theoretical basis of the algorithm used is established. The importance of well-designed diagnostic facilities in decision table processors is emphasized. [ABSTRACT FROM AUTHOR]

Spinning tops and yo-yos have long fascinated cultures around the world with their unexpected, graceful motions that seemingly elude gravity. Yet, due to the exceeding difficulty of creating stably spinning objects of asymmetric shape in a manual trial-and-error process, there has been little departure from rotationally symmetric designs. With modern 3D printing technologies, however, we can manufacture shapes of almost unbounded complexity at the press of a button, shifting this design complexity toward computation. In this article, we describe an algorithm to generate designs for spinning objects by optimizing their mass distribution: as input, the user provides a solid 3D model and a desired axis of rotation. Our approach then modifies the interior mass distribution such that the principal directions of the moment of inertia align with the target rotation frame. To create voids inside the model, we represent its volume with an adaptive multiresolution voxelization and optimize the discrete voxel fill values using a continuous, nonlinear formulation. We further optimize for rotational stability by maximizing the dominant principal moment. Our method is well-suited for a variety of 3D printed models, ranging from characters to abstract shapes. We demonstrate tops and yo-yos that spin surprisingly stably despite their asymmetric appearance. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms. [ABSTRACT FROM AUTHOR]

This paper describes the development of an energy-efficient amplify-and-forward distributed estimation scheme using realistic amplifier models. Specifically, a novel algorithm is presented that enables distributed estimation in the presence of amplifier compression resulting from the energy-efficient but non-linear class AB operation. In this system, a digital predistortion scheme is utilized to fit the amplifier at each sensor to a mathematically tractable, soft compression function that roughly mimics the compression region of the amplifier. It is shown both analytically and via simulation that using this scheme has two benefits over linear amplifier operation: improved transmitter efficiency by operating the amplifier in compression, and reduced sensitivity to heavy-tailed distributions due to the soft saturation. [ABSTRACT FROM AUTHOR]

As linear programming models grow bigger and bigger in size, much actual data that must be memorized is often put on magnetic tape or disk, and consequently there is an improportionately fast rise in the consumption of computer time. To cut down this expense, on ever increasing effort Is mode to design more efficient algorithms. This paper is meant to support the effort. If is attempted to find some characteristics of the way pivot column is found. The number of repetitions of a certain transfer of data from tape to core memory is considered. After some simplification, the problem is restated in a general way. The generating function of the probability distribution and the moment generating function of the number of repetitions is found. Asymptotic formulas are given for the moments using result from a paper of S. Narumi [1]. The results may be applied to write very efficient routines that search for an extreme value in a table. Formulas provide a means of calculating the computer timings in this case. [ABSTRACT FROM AUTHOR]

In this paper, we present complexity certification results for a distributed augmented Lagrangian (AL) algorithm used to solve convex optimization problems involving globally coupled linear constraints. Our method relies on the accelerated distributed AL (ADAL) algorithm, which can handle the coupled linear constraints in a distributed manner based on local estimates of the AL. We show that the theoretical complexity of ADAL to reach an $\epsilon$-optimal solution both in terms of suboptimality and infeasibility is O(\frac{1}{\epsilon }) iterations. Moreover, we provide a valid upper bound for the optimal dual multiplier, which enables us to explicitly specify these complexity bounds. We also show how to choose the step-size parameter to minimize the bounds on the convergence rates. Finally, we discuss a motivating example, a model predictive control problem, involving a finite number of subsystems, which interact with each other via a general network. [ABSTRACT FROM PUBLISHER]

This paper generalizes the well-known covariance intersection algorithm for distributed estimation and information fusion of random vectors. Our focus will be on partially correlated random vectors. This is motivated by the restriction of the standard covariance intersection algorithm, which treats all random vectors with arbitrary cross correlations and the restriction of the classical Kalman filter, which requires complete knowledge of the cross correlations. We first give a result to characterize the conservatism of the standard covariance intersection algorithm. We then generalize the covariance intersection algorithm to two random vectors with a given correlation coefficient bound and show in what sense the resulting covariance bound is tight. Finally, we generalize the notion of correlation coefficient bound to multiple random vectors and provide a covariance intersection algorithm for this general case. Our results will make the already popular covariance intersection more applicable and more accurate for distributed estimation and information fusion problems. [ABSTRACT FROM PUBLISHER]

In this paper, source localization by a network of primitive binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented. Imperfections include sensor failures of two types, drifting, uncertainty, and heterogeneity in binary sensor trigger thresholds, presence of noise, and nonradial symmetry of sensing ranges. Theoretical results, including asymptotical convergence, are established, in particular in the presence of substantial outliers due to sensor failure and large noise. Efficient numerical algorithms are proposed and simulated supporting the theoretical analysis. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to attempt to realize a distribution network optimization in supply chain using grey systems theory for uncertain information. Design/methodology/approach – There is much uncertain information in the distribution network optimization of supply chain, including fuzzy information, stochastic information and grey information, etc. Fuzzy information and stochastic information have been studied in supply chain, however grey information of the supply chain has not been covered. In the distribution problem of supply chain, grey demands are taken into account. Then, a mathematics model with grey demands has been constructed, and it can be transformed into a grey chance-constrained programming model, grey simulation and a proposed hybrid particle swarm optimization are combined to resolve it. An example is also computed in the last part of the paper. Findings – The results are convincing: not only that grey system theory can be used to deal with grey uncertain information about distribution of supply chain, but grey chance-constrained programming, grey simulation and particle swarm optimization can be combined to resolve the grey model. Practical implications – The method exposed in the paper can be used to deal with distribution problems with grey information in the supply chain, and network optimization results with a grey uncertain factor could be helpful for supply chain efficiency and practicability. Originality/value – The paper succeeds in realising both a constructed model of the distribution of supply chain with grey demands and a solution algorithm of the grey mathematics model by using one of the newest developed theories: grey systems theory. [ABSTRACT FROM AUTHOR]

Classical methods for estimating additive faults are based on state-space models, e.g., moving horizon estimation (MHE) and unknown input observers (UIOs). This paper contributes new direct design methods from closed-loop I/O data for systems with stable inversion, which do not require building a state-space model by first principles, nor require identifying it. Inspired by subspace identification, we use the input and output (I/O) relationship of a plant in a Vector ARX (VARX) form to parameterize least-squares (LS) problems for estimating faults. We prove that with the order of the VARX descriptions tending to infinity, the fault estimates are unbiased. Under lower relative degrees, we prove that our new methods are equivalent to system-inversion-based estimation for both LTI and LTV systems. We will show more general unbiased estimation conditions for higher relative degrees. These require that the underlying inverted system from faults to outputs is stable. Algorithms of identifying unbiased fault estimation filters from data will be developed in this paper based on single LS. Moreover, covariance of the fault estimates can also be extracted from data. [ABSTRACT FROM AUTHOR]

Purpose – The novel bearingless switched reluctance motor (BSRM) is proposed recently, which is different from the conventional BSRM in the stator structure and suspension winding arrangement. The reduced number of suspension windings makes the novel BSRM much simpler, so that the control circuit and algorithm are greatly simplified when compared to those of the conventional BSRM. This paper for the first time proposes the novel BSRM's analytic model, including the mathematical relationships among the winding currents, rotor angle, radial forces, and motor torque, to further achieve the suspending forces and torque control. The paper aims to discuss these issues. Design/methodology/approach – The magnetic equivalent circuit method is employed to obtain the self-inductances and mutual-inductances of the motor torque windings (main windings) and suspension windings (control windings). The straight flux paths are combined with the elliptical fringing flux paths to calculate the air-gap permeances, and the stored magnetic energy. Then, the mathematical expressions of radial forces and torque are derived. A novel BSRM prototype is analyzed through using the proposed analytical model and the finite element model. The results of both methods are compared to verify the proposed mathematical model. Findings – The proposed mathematical model of the novel BSRM considering unsaturated magnetic circuits is verified by finite-element analysis results. Research limitations/implications – The mathematical model represents the situation of magnetic circuit unsaturated and is not suitable for the magnetic circuit saturation. It cannot be used to control the motor which is working in the deep magnetic circuit saturation region. Practical implications – Building mathematical model is a necessary step for the motor's suspension and rotating control. The built model provides the fundamental for the preliminary control algorithm and experimental study of this novel BSRM. Originality/value – For the first time, the novel BSRM's mathematical model is proposed. It provides necessary fundamental for the motor's further analysis, design, and suspending and rotating controls. [ABSTRACT FROM AUTHOR]

In this paper, we continue to explore application of nonsmooth analysis to the study of global asymptotic robust stability (GARS) of delayed neural networks. In combination with Lyapunov theory, our approach gives several new types of sufficient conditions ensuring GARS. A significant common aspect of our results is their low computational complexity. It is demonstrated that the reported results can be verified either by conducting spectral decompositions of symmetric matrices associated with the uncertainty sets of network parameters, or by solving a semidefinite programming problem. Nontrivial examples are constructed to compare with some closely related existing results. [ABSTRACT FROM AUTHOR]

This paper presents a new theory that addresses the issue of passivity in macromodels of electrical interconnects constructed based on the method of characteristics (MoC). The proposed approach develops a new algebraic test to check for passivity in macromodels generated using MoC. The theory behind the developed test is based on deriving the necessary and sufficient conditions for the loss of positive-realness in the admittance matrix of the developed macromodel. An algorithmic procedure is proposed to verify passivity of general macromodels derived from the MoC. The results presented in this paper can be employed to test for positive-realness in dynamical systems described by algebraic delay-differential equations with discrete commensurate delays. [ABSTRACT FROM AUTHOR]

Evaluates algorithmic models used to estimate software costs. Data on completed business data processing projects; Comparison of the actual costs with the ex post estimates abstained from models; Different measures of the outputs underlying their productivity calculations.

Purpose – Variable density flows play an important role in many technological devices and natural phenomena. The purpose of this paper is to develop a robust and accurate method for low Mach number flows with large density and temperature variations. Design/methodology/approach – Low Mach number approximation approach is used in the paper combined with a predictor-corrector method and accurate compact scheme of fourth and sixth order. A novel algorithm is formulated for the projection method in which the boundary conditions for the pressure are implemented in such a way that the continuity equation is fulfilled everywhere in the computational domain, including the boundary nodes. Findings – It is shown that proposed implementation of the boundary conditions considerably improves a solution accuracy. Assessment of the accuracy was performed based on the constant density Burggraf flow and for two benchmark cases for the natural convection problems: steady flow in a square cavity and unsteady flow in a tall cavity. In all the cases the results agree very well with exemplary solutions. Originality/value – A staggered or half-staggered grid arrangement is usually used for the projection method for both constant and low Mach number flows. The staggered approach ensures stability and strong pressure-velocity coupling. In the paper a high-order compact method has been implemented in the framework of low Mach number approximation on collocated meshes. The resulting algorithm is accurate, robust for large density variations and is almost free from the pressure oscillations. [ABSTRACT FROM AUTHOR]

Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data. This aspect is of paramount relevance within the big data framework, since measurements are there immediately processed to detect and identify the upsurge of potential faults. This paper introduces a novel cognitive fault diagnosis framework for processes described by nonlinear dynamic systems that inspects changes in the existing relationships among sensors. The proposed framework is based on an evolving clustering algorithm that operates in the parameter space of time invariant linear models approximating such relationships. During the operational life, parameter vectors associated with models thought not to belong to the nominal state are either labeled as outlier or fault. New classes of faults, here considered to propagate to the model parameters according to an abrupt profile, are created online as they appear. At the same time, existing classes can merge, depending on the information content carried by incoming data. [ABSTRACT FROM AUTHOR]

In this paper, we present adaptive and distributed algorithms for motion coordination of a group of m vehicles. The vehicles must service demands whose time of arrival, spatial location, and service requirement are stochastic; the objective is to minimize the average time demands spend in the system. The general problem is known as the m-vehicle Dynamic Traveling Repairman Problem (m-DTRP). The best previously known control algorithms rely on centralized task assignment and are not robust against changes in the environment. In this paper, we first devise new control policies for the 1-DTRP that: i) are provably optimal both in light-load conditions (i.e., when the arrival rate for the demands is small) and in heavy-load conditions (i.e., when the arrival rate for the demands is large), and ii) are adaptive, in particular, they are robust against changes in load conditions. Then, we show that specific partitioning policies, whereby the environment is partitioned among the vehicles and each vehicle follows a certain set of rules within its own region, are optimal in heavy-load conditions. Building upon the previous results, we finally design control policies for the m-DTRP that i) are adaptive and distributed, and ii) have strong performance guarantees in heavy-load conditions and stabilize the system in any load condition. [ABSTRACT FROM AUTHOR]

This paper studies the statistical behavior of the normalized subband adaptive filtering (NSAF) algorithm. An accurate statistical model of the NSAF algorithm is obtained. In the derivation, we focus on Gaussian correlated input signals. By assuming that the analysis filter bank is paraunitary and taking into account the full band adaptation mechanism of the NSAF, expressions for the first and the second moments of the adaptive filter weights are derived without invoking the slow adaptation assumption. In the derivations, several hyperelliptic integrals appear. To tackle those integrals induced by Gaussian correlated inputs, we first give a solution by resorting to the adaptive Lobatto quadrature. By invoking the averaging principle, two other approximation methods, the chi-square method and the partial fraction expansion method, are presented to approximate the statistical model as well. Monte Carlo (MC) simulation results corroborate our predictions. The Lobatto quadrature method achieves a good agreement with the MC simulation results, even for a relatively large step size. Compared with the chi-square method and the partial fraction expansion method, the Lobatto quadrature method gives better performance in terms of predicting the mean square error when the length of the adaptive filters is small to medium. The chi-square approximation method and the partial fraction expansion method give a satisfactory performance with a relatively low computational complexity when the filter length is large. [ABSTRACT FROM PUBLISHER]

Abstract-In this paper, the problem of designing robust supervisory controllers for uncertain discrete event systems is studied. It is assumed that both the plant behavior and the design specifications can be represented by regular languages. It is also assumed that the unknown model of the uncertain plant belongs to a finite set of possibilities. To solve the problem, a modular approach based on a general recursive robust control scheme is developed. The contribution of the present paper consists in deriving a maximally permissive modular robust supervisory controller for the case of prefix.closed languages, having the advantage of linear complexity in the number of models as opposed to the exponential complexity of the worstcase scenario in the direct implementation of standard supervisors for aug. mented systems. [ABSTRACT FROM AUTHOR]

This paper is motivated by practical consideration that the input updating and output sampling rates are often limited due to sensor and actuator speed constraints. In particular, for general dual-rate systems with different updating and sampling periods, we derive the lifted state-space models (mapping relations between available dual-rate input-output data), and, by using a hierarchical identification principle, present combined parameter and state estimation algorithms for identifying the canonical lifted models based on the given dual-rate input-output data, taking into account the causality constraints of the lifted systems. Finally, we give an illustrative example to indicate that the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

Keywords: Algorithms; Health Services; Health and Medicine; Mathematics EN Algorithms Health Services Health and Medicine Mathematics 4096 4096 1 09/19/23 20230922 NES 230922 2023 SEP 22 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- New study results on health services have been published. Algorithms, Health Services, Health and Medicine, Mathematics. [Extracted from the article]

This paper concerns decentralized control of a multi-agent stochastic queueing system. The system consists of two agents, a pricing agent and a service agent. The pricing agent controls the arrival rate by dynamically setting the fee that is paid by customers who would like to receive service, while the service agent controls the rate at which customers in the system are processed. Each agent optimizes his/her own objective subject to a model which accurately specifies the component of the system that he/she is controlling but potentially mis-specifies the component that is controlled by the other agent. We are interested in optimizing the efficiency of the aggregate system when control decisions are made in this way. Decentralized agents are coordinated using transfer contracts, which define a price for occupying unit of buffer space in the system as a function of time and available capacity. Transfer contracts lead to cash transfers between agents whenever a customer enters or departs, and serve to modify each of their incentives. We characterize the contract that maximizes the efficiency of the aggregate system under decentralized control, and show that it achieves the efficiency of a centralized agent who jointly optimizes over pricing and service policies with an accurate model of the integrated system. We also show that optimal centralized efficiency is robust to mis-specification by each agent of the dynamics induced by the other in his/her model. An iterative decentralized algorithm for constructing the optimal contract and a proof of convergence is also presented. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to present a detailed algorithm for simulating three-dimensional hydrocarbon reservoirs using the blackoil model. Design/methodology/approach -- The numerical algorithm uses a cell-centred structured grid finite volume method. The blackoil formulation is written in a way that an Implicit Pressure Explicit Saturation approach can be used. The flow field is obtained by solving a general gas pressure equation derived by manipulating the governing equations. All possible variations of the pressure equation coefficients are given for different reservoir conditions. Key computational details including treatment of non-linear terms, expansion of accumulation terms, transitions from under-saturated to saturated states and vice versa, high gas injection rates, evolution of gas in the oil production wells and adaptive time-stepping procedures are elaborated. Findings -- It was shown that using a proper linearization method, less computational difficulties occur especially when free gas is released with high rates. The computational performance of the proposed algorithm is assessed by solving the first SPE comparative study problem with both constant and variable bubble point conditions. Research limitations/implications -- While discretization is performed and implemented for unstructured grids, the numerical results are presented only for structured grids, as expected, the accuracy of numerical results are best for structured grids. Also, the reservoir is assumed to be non-fractured. Practical implications -- The proposed algorithm can be efficiently used for simulating a wide range of practical problems wherever blackoil model is applicable. Originality/value -- A complete and detailed description of ingredients of an efficient finite volume-based algorithm for simulating blackoil flows in hydrocarbon reservoirs is presented. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present some results of the development of parametrical regulation theory elements for computable general equilibrium models (CGE models), taking into consideration their peculiarities. Design/methodology/approach – Theoretical results have been obtained by means of applying geometrical methods for variational problems and methods of the theory of discrete dynamic systems. These results have been used for solving concrete practical problems. Findings – The authors proved a statement on the existence of solution for variational calculus problem on the choice of the optimal laws of parametrical regulation within the given finite set of algorithms for discrete dynamic systems. A statement has been proved on sufficient conditions for the existence of an extremal's bifurcation point of variational calculus problem on the choice of the optimum laws of parametrical regulation within the given finite set of algorithms for discrete dynamic systems. Optimal laws of parametrical regulation (on the level of one and two parameters) of economic system evolution on the basis of the examined mathematical model have been defined. The bifurcation line was constructed for the given area of uncontrolled parameter values. Practical implications – The research results can be applied for the choice and realization of an effective state budget and tax policy. Originality/value – The paper elaborates the elements of parametrical regulation theory of economic system development on the basis of CGE models and shows the effectiveness of parametrical regulation theory application on the example of one CGE model. [ABSTRACT FROM AUTHOR]

In this paper, first, we investigate the CNN templates producing periodic patterns. Next, two image processing algorithms using periodic pattern formation in cellular neural networks are proposed. By applying one to a certain image, area having spatial uniformity is removed from the original image and only parts without spatial uniformity remain. Simulation results show that when this algorithm is applied to an image containing characters (texts) or a human face image, only characters or eyes and mouth are extracted. Also, by applying the other to texture image, periodicity is enhanced in area having spatial periodicity, and texture can be segmented. Simulation results show that when this algorithm is applied to image that consists of four kinds of textures, each texture area can be segmented. [ABSTRACT FROM AUTHOR]

We consider the problem of choosing the gate sizes or scale factors in a combinational logic circuit in order to minimize the total area, subject to simple RC timing constraints, and a minimum-allowed gate size. This problem is well known to be a geometric program (GP), and can be solved by using standard interior-point methods for small- and medium-size problems with up to several thousand gates. In this paper, we describe a new method for solving this problem that handles far larger circuits, up to a million gates, and is far faster. Numerical experiments show that our method can compute an adequately accurate solution within around 200 iterations; each iteration, in turn, consists of a few passes over the circuit. In particular, the complexity of our method, with a fixed number of iterations, is linear in the number of gates. A simple implementation of our algorithm can size a 10000 gate circuit in 25 s, a 100 000 gate circuit in 4 mm, and a million gate circuit in 40 mm, approximately. For the million gate circuit, the associated GP has three million variables and more than six million monomial terms in its constraints; as far as we know, these are the largest GPs ever solved. [ABSTRACT FROM AUTHOR]

This paper presents a constructive method for (sub)optimal finite-impulse response (FIR) approximation of infinite-impulse response (IIR) models. The method minimizes the Hankel norm of the approximation error by using an explicit solution to the norm-preserved dilation problem. It has advantages over the existing methods in that it is a unified method for both single-input single-output and multiple-input multiple-output systems which allows direct tradeoff between .the least-squares and Chebyshev error criteria by using a single tuning parameter, and that the approximation algorithm is constructive and only involves algebraic manipulations rather than iteration and convex optimization procedures. The lower and upper bounds on the l² and Chebyshev norms of the approximation error are derived and are related to the tuning parameter. The problem of approximating noncausal IIR models by causal FIR models is also formulated and solved. The effectiveness and properties of the proposed algorithms are demonstrated by examples. [ABSTRACT FROM AUTHOR]

We present a nonsmooth output feedback framework for local and/or global stabilization of a class of nonlinear systems that are not smoothly stabilizable nor uniformly observable. A systematic design method is presented for the construction of stabilizing, dynamic output compensators that are nonsmooth but Hölder continuous. A new ingredient of the proposed output feedback control scheme is the introduction of a recursive observer design algorithm, making it possible to construct a reduced-order observer step-by-step, in a naturally augmented manner. Such a nonsmooth design leads to a number of new results on output feedback stabilization of nonlinear systems. One of them is the global stabilizability of a chain of odd power integrators by Hölder continuous output feedback. The other one is the local stabilization using nonsmooth output feedback for a wide class of nonlinear systems in the Hessenberg form studied in a previous paper, where global stabilizability by nonsmooth state feedback was already proved to be possible. [ABSTRACT FROM AUTHOR]

This paper treats the problem of identification of a linear electric circuit described by an ill-conditioned mathematical model. The identification problem is considered as the problem of model parameters determination by means of processing experimental data measured for the objective circuit. Topological singularities (low-admittance cutsets and low-impedance loops) in a circuit are found to be origins of ill-conditionality of a circuit model. For more in-depth investigation the classification of electric circuits is made in respect to singularities position. It is shown that the first set of experimental data obtained for an ill-conditioned model is useless for getting the required solution of the identification problem. In this case a solution error amounts to a huge value that exponentially increases with growth in condition number of a model matrix. It is found that linear relations between model parameters can be determined in an ill-conditioned problem. Accuracy of these relations does not depend on condition number, but is defined only by measurement precision. An approach named as repeat measurements principle (RMP) and based on linear relations is suggested to solve an ill-conditioned identification problem. A new RMP-based algorithm of linear circuit identification is developed. The algorithm shows a high efficiency and allows us to determine model parameters accurate to measurement precision as applied to any type of reciprocal and nonreciprocal linear circuits. [ABSTRACT FROM AUTHOR]

This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static none linearities follow from a low-rank approximation of a matrix obtained from this regression problem. [ABSTRACT FROM AUTHOR]

This paper presents a new statistical analysis of the affine projection (AP) algorithm. An analytical model is derived for autoregressive (AR) inputs for unity step size (fastest convergence). Deterministic recursive equations are derived for the mean AP weight and mean-square error for large values of N (the number of adaptive taps). The value of N is also assumed large compared to the algorithm order (number of input vectors used to determine the weight update direction). The model predictions display better agreement with Monte Carlo simulations in both transient and steady-state than models previously presented in the literature. The model's accuracy is sufficient for most practical design purposes. [ABSTRACT FROM AUTHOR]

The article presents a dynamic computer simulation method for the physical phenomena of complex collisions of objects for use in computer graphics animation. The challenges of developing an asynchronous integration algorithm for the contact mechanics of simulated collisions are discussed, including topics such as collision detection, asynchronous variational integrators (AVIs), and simple discrete penalty layers. The simulation system is tested with various parameters in order to assess its safety, correctness, and progress.

We present Goldilocks, a Java runtime that monitors program executions and throws a DataRaceException when a data race is about to occur. This prevents racy accesses from taking place, and allows race conditions to be handled before they cause errors that may be difficult to diagnose later. The DataRaceException is a valuable debugging tool, and, if supported with reasonable computational overhead, can be an important safety feature for deployed programs. Experiments by us and others on raceaware Java runtimes indicate that the DataRaceException may be a viable mechanism to enforce the safety of executions of multithreaded Java programs. An important benefit of DataRaceException is that executions in our runtime are guaranteed to be race free and thus sequentially consistent as per the Java Memory Model. This strong guarantee provides an easy-to-use, clean semantics to programmers, and helps to rule out many concurrency- related possibilities as the cause of errors. To support the DataRaceException, our runtime incorporates the novel Goldilocks algorithm for precise dynamic race detection. The Goldilocks algorithm is general, intuitive, and can handle different synchronization patterns uniformly. [ABSTRACT FROM AUTHOR]

Individual judgment and initiative are essential to the success of the modern capitalist economy. At the same time, rules and centralized systems are needed to bring order and prevent waste. Balancing decentralization and command-and-control modes of decision making has always been a struggle, and organizations have experience managing the tension. In recent times, though, a new form of centralized control has taken root: mechanistic decision making based on top-down statistical models and algorithms. This has been especially true in finance, where risk models have replaced the judgments of thousands of individual bankers and investors, to disastrous effect. The problem with the statistical approach is that it cannot adequately account for the uncertainty inherent in economic decisions or the idiosyncratic nature of human activity. What finance in particular needs is a return to judgment. The author offers some broad guidelines for this: Computerized controls work best with inanimate products and processes that can be physically shielded so that the variations of conditions can be minimized. Computers also shine when, like the configuration of pieces on a chess set, the number of possible outcomes is vast -- in fact, this vastness often gives the computer its edge -- but they all conform to well-specified rules. Conversely, human judgment is favored when shielding is difficult, outcomes are ambiguous, and the possibilities are open-ended. Ultimately, however, the “rightsizing” of judgment is itself a matter of judgment. INSET: Fixing Financial Services. [ABSTRACT FROM AUTHOR]

Dark pools are a recent type of stock exchange in which information about outstanding orders is deliberately hidden in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools have created challenging and interesting problems in algorithmic trading—in particular, the problem of optimizing the allocation of a large trade over multiple competing dark pools. In this work, we formalize this optimization as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. Our algorithm and its analysis have much in common with well-studied algorithms for managing the exploration—exploitation trade-off in reinforcement learning. We also provide an extensive experimental evaluation of our algorithm using dark pool execution data from a large brokerage. [ABSTRACT FROM AUTHOR]

Keywords: Algorithms; Brachytherapy; Drugs and Therapies; Health and Medicine; Mathematics; Radiotherapy; Therapy EN Algorithms Brachytherapy Drugs and Therapies Health and Medicine Mathematics Radiotherapy Therapy 1259 1259 1 08/14/23 20230818 NES 230818 2023 AUG 15 (NewsRx) -- By a News Reporter-Staff News Editor at Drug Week -- Research findings on radiotherapy are discussed in a new report. With the expansion of using HDR-High dose rate Brachytherapy and LDR-low dose rate Brachytherapy for the treatment of cancer, it requires fractionated does treatment plan to irradiate the tumor. [Extracted from the article]

Abstracts are presented for papers published in the journal including for "Attack Detection and Identification in Cyber-Physical Systems," by F. Pasqualetti, F. Dörfler and F. Bullo, "Nonuniform Coverage Control On The Line," by N. E. Leonard and A. Olshevsky, and "Coordinated One-Step Optimal Distributed State Prediction For A Networked Dynamical System," by T. Zhou.

Purpose – The purpose of this paper is to introduce a method based on the fuzzy correlation for modelling of active and reactive powers from the substations of the electrical distribution systems, at the peak load. Design/methodology/approach – Based on the correlation theory, the fuzzy models of the loads can be obtained using a new algorithm. If in the case of the principal/connection station there is sufficient database information for a good forecasting of the load, then for those substations where data are missing (there is no continuous monitoring or the measuring system can be broken for a while) the forecasting of the load can be performed using the correlation studies. The starting point of the algorithm is statistical analysis of the active and reactive curves of the substations and utilization of a fuzzy linear regression model. This can be made for different time windows (window 24 h, window 7 h, etc). The window 24 h can be used successfully to estimate the hourly load on any substation. The other time window (7 h) can be used in the peak load estimation of the substations, using the maximum value of the active power recorded in a reference substation. Findings – The numerical data show that the fuzzy correlation models can be used with very good results for determination of the peak load corresponding distribution substations, and further with the state estimation of the system. In this study, the influence of the time window size is presented in detail, and the fuzzy correlation models for the peak loads from the distribution substations are obtained. Originality/value – Starting from the correlation theory, a method of fuzzy modelling of active and reactive powers from the substations of an electrical distribution system is proposed. [ABSTRACT FROM AUTHOR]