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The paper addresses nonlinear estimation problems on nonlinear processes containing several lab measurements sampled slowly and with long delay, which is the usual case in industrial polymerization applications. A moving horizon estimation algorithm is developed to compute the theoretical optimal solution given the multi-rate measurements. In this algorithm, the MHE window is recalculated as the new lab measurement becomes available. Simulation studies on a polymerization process with plant model mismatch are performed. Observability analysis and estimation results of MHE with and without lab measurements show that lab measurements help identify the disturbances and can improve the performance of both estimation and closed-loop control. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of trajectory tracking control for underactuated autonomous underwater vehicles (AUVs) in 3 dimensions space. Given a smooth, inertial, 3D reference trajectory, in order to make the position error to stay in the neighborhood of zero, the control algorithm uses vehicle׳s kinematic equation and linear system stability theory to compute the desired body-fixed velocities. Using these methods, the velocity error dynamics are obtained. Backstepping techniques are used, forcing the tracking error to an arbitrarily small neighborhood of zero. Simulation of a spiral trajectory is performed. The result shows that the controller can realize 3 dimensions trajectory tracking effectively. [ABSTRACT FROM AUTHOR]

Multi-agent technology is applied in this paper to solve the distribution system service restoration problems. Specifically, switches in the distribution systems are modeled as intelligent agents and organized as local power committees in a distribution system. The advantage of this approach is to utilize the solving ability of a local committee of agents who could coordinate with neighboring committees to achieve an agreement that satisfies the global objective of the power distribution system service restoration problems without relying on a pre-determined centralized optimization algorithm as traditional approaches. The proposed coordination method helps switches in the distributed local power committees to reach a consensus among many proposed service restoration solutions after faults are detected and isolated. We also take the voltage violations into consideration in the search of solutions. Two examples of different power distribution systems are presented to demonstrate how the algorithms of the coordination method can identify the best restoration solution among many proposed local candidate solutions. The simulation results show that the proposed committee-based multi-agent approach yields better performance than the previous methods. [ABSTRACT FROM AUTHOR]

This paper deals with the computational modeling and numerical simulation of contact problems at finite deformations using the finite element method. Quasi-static and dynamic problems are considered and two particular frictional conditions, full stick friction and frictionless cases, are addressed. Lagrange multipliers and regularized formulations of the contact problem, such as penalty or augmented Lagrangian methods, are avoided and a new direct elimination method is proposed. Conserving algorithms are also introduced for the proposed formulation for dynamic contact problems. An assessment of the performance of the resulting formulation is shown in a number of selected benchmark tests and numerical examples, including both quasi-static and dynamic contact problems under full stick friction and frictionless contact conditions. Conservation of key discrete properties exhibited by the time stepping algorithm used for dynamic contact problems is also shown in an example. [ABSTRACT FROM AUTHOR]

L 1 regularization and L p regularization are proposed for processing recovered images based on compressed sensing (CS). L 1 regularization can be solved as a convex optimization problem but is less sparse than L p (0 < p < 1). L p regularization is sparser than L 1 regularization but is more difficult to solve. This paper proposes joint L 1 / L p (0 < p < 1) regularization, which combines L p regularization and L 1 regularization. This joint regularization is applied to recover video of remote sensing based on CS. Joint regularization is sparser than L 1 regularization but is as easy to solve as L 1 regularization. A linearized Bregman reweighted iteration algorithm is proposed to solve the joint L 1 / L p regularization problem. The performance and capabilities of the linearized Bregman algorithm and linearized Bregman reweighted algorithm for solving the joint L 1 / L p regularization model are analyzed and compared through numerical simulations. [ABSTRACT FROM AUTHOR]

DV-Hop is one of the range-free localization algorithms using hop-distance estimation. However, it suffers from a large localization deviation and low localization accuracy. This paper proposed a weight-based DV-HOP improved algorithm. It improves the traditional DV-HOP algorithm mainly in the following aspects. First, it increases the calculation accuracy of hop counts. Second, it corrects the average hop distance calculated by the anchor node by using least square method. Third, the un-located nodes calculate the distance to other anchor nodes based on the link state with weights. The simulation results showed that the proposed algorithm significantly reduces the localization deviation, with a small increase in the node's computation and communication cost. It is superior to the existing DVHOP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel protection of coordinating optimization algorithm is proposed. Maximizing the penetration's dispersed generation and at the same time minimizing the fuse's operating time are the targets. A novel optimization technique, the Imperialistic Competition Algorithm (ICA), is applied to solve the problem. The results of simulations confirm that the proposed method leads to lower operating times of protective devices and higher possible DG penetration, compared with the traditional coordination techniques. [ABSTRACT FROM AUTHOR]

Abstract: Assigning tasks to a set of robots is a fundamental problem in robotics. It consists in finding the best task assignment to the available robots. In this paper, we present two distributed market-based algorithms to solve the assignment problem where n robots compete for n tasks with the assumption that each robot can be assigned to only one task. The first algorithm, called DMB, represents a Distributed Market-Based algorithm where each robot bids for every task. The second algorithm is an extension of the DMB. It consists in swapping tasks between robots in order to improve the efficiency of the whole assignment. We conducted both real-world experimental testing, and MATLAB simulations to evaluate performance of the proposed algorithms and compare them against the centralized Hungarian algorithm in terms of traveled distance. Simulation results show that the IDMB algorithm produces near optimal solutions and in several cases it gives the optimal solution. In addition, we demonstrated the feasibility of our algorithms through real-world experimentation on robots. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of clustering based procedure for the identification of PieceWise Auto-Regressive eXogenous (PWARX) models is addressed. This problem involves both the estimation of the parameters of the affine sub-models and the hyperplanes defining the partitions of the state-input regression. In fact, we propose the use of the Chiu's clustering algorithm in order to overcome the main drawbacks of the existing methods which are the poor initialization and the presence of outliers. In addition, our approach is able to generate automatically the number of sub-models. Simulation results are presented to illustrate the performance of the proposed method. An application of the developed approach to an olive oil esterification reactor is also suggested in order to validate simulation results. [Copyright &y& Elsevier]

Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same. [ABSTRACT FROM AUTHOR]

LAVs (loitering air vehicles) are advanced weapon systems that can loiter autonomously over a target area, detect and acquire the targets, and then attack them. In this paper, by the theory of Itô stochastic differential, a group systemwas analyzed. The uniqueness and continuity of the solution of the system was discussed. Afterwards the model of the system based on the state transition was establishedwith the finite statemachine automatically. At last, a search algorithmwas proposed for obtaining good feasible solutions for problems. And simulation results show that model and method are effective for dealing with cooperative combat of group LAVs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze load-carrying capacity of redundant free-floating space manipulators (FFSM) in trajectory tracking task. Combined with the analysis of influential factors in load-carrying process, evaluation of maximum load-carrying capacity (MLCC) is described as multiconstrained nonlinear programming problem. An efficient algorithm based on repeated line search within discontinuous feasible region is presented to determine MLCC for a given trajectory of the end-effector and corresponding joint path. Then, considering the influence of MLCC caused by different initial configurations for the starting point of given trajectory, a kind of maximum payload initial configuration planning method is proposed by using PSO algorithm. Simulations are performed for a particular trajectory tracking task of the 7-DOF space manipulator, of which MLCC is evaluated quantitatively. By in-depth research of the simulation results, significant gap between the values of MLCC when using different initial configurations is analyzed, and the discontinuity of allowable load-carrying capacity is illustrated. The proposed analytical method can be taken as theoretical foundation of feasibility analysis, trajectory optimization, and optimal control of trajectory tracking task in on-orbit load-carrying operations. [ABSTRACT FROM AUTHOR]

Cooperative trajectory mapping is an emerging technique that allows users to create such a map by using data that are collected from each other's mobile phones. This technique has been proposed for many applications, such as people localisation, public transportation tracking and traffic monitoring. Unlike the traditional Global Positioning System, cooperative trajectory mapping only requires information about the departure distance and moving direction from the previously reported position. This avoids problems such as the high-energy consumption of GPS and weak GPS signals in indoor conditions. However, the new technique also brings about other problems, such as measurement errors in cooperative trajectory mapping, which is when a measurement error causes the spatial relations among users to be wrong. We propose an encounter-based error correction algorithm to efficiently reduce measurement errors. Extensive simulation experiments are carried out to validate our solutions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, finite-time position consensus and collision avoidance problems are investigated for multi-AUV (autonomous underwater vehicle) systems. First, based on the homogeneous control method, finite-time position consensus algorithms are proposed for both leaderless and leader–follower multi-AUV systems without considering collisions between the AUVs. Specifically, in the leader–follower case, a novel distributed finite-time observer is developed for the followers to estimate the leader’s velocity. Second, by constructing collision avoidance and connectivity maintenance functions, modified consensus algorithms containing corresponding gradient terms are presented for multi-AUV systems of both cases, which guarantee collision avoidance, connectivity maintenance, velocity matching, and consensus boundedness. Simulations demonstrate the effectiveness of the proposed control algorithms. [Copyright &y& Elsevier]

Abstract: This paper investigates a flocking problem of multiple agents with heterogeneous nonlinear dynamics. In order to avoid fragmentation, we construct a potential function and a connectivity-preserving flocking algorithm to enable the multiple agents to move with the same velocity while preserving the connectivity of underlying networks with a mild assumption that the initial network is connected and the coupling strength of the initial network of the nonlinear velocity consensus term is larger than a threshold value. Furthermore the proposed flocking algorithm is extended to solve the problem of multi-agent systems with a nonlinear dynamical virtual leader. The result is that all agents' velocities asymptotically approach to the velocity of the virtual leader, and the distance between any two agents is asymptotically stabilized to avoid collisions among agents. Finally, some numerical simulations are presented to illustrate the effectiveness of the theoretical results. [Copyright &y& Elsevier]

Abstract: This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization. [Copyright &y& Elsevier]

Abstract: The maximal covering location problem (MCLP) seeks location of facilities on a network, so as to maximize the total demand within a pre-defined distance or travel time of facilities (which is called coverage radius). Most of the real-world applications of MCLP comprise many demand nodes to be covered. Moreover, uncertainty is ubiquitous in most of the real-world covering location problems, which are solved for a long-term horizon. Therefore, this paper studies a large-scale MCLP on the plane with fuzzy coverage radii under the Hurwicz criterion. In order to solve the problem, a combination of variable neighborhood search (VNS) and fuzzy simulation is offered. Test problems with up to 2500 nodes and different settings show that VNS is competitive, since it is able to find solutions with gaps all below 1.5% in much less time compared to exact algorithms. [Copyright &y& Elsevier]

Abstract: As one of the most fundamental problems in open-membership systems, Sybil attack has attracted wide attentions from both industrial and academic fields. Many anonymous communication and censorship-resistant systems have incorporated Sybil defense in their designs based on social networks. This paper proposed two-class undirected mixed membership stochastic blockmodels to discover adversary's Sybil identities in the network. Different from existing algorithms, our model discriminates Sybil nodes by simulating the generative process of social networks. And we gave a matrix B to describe the interaction probability of Sybil and honest nodes, which can be used to observe contaminated situation of the network. The effectiveness of the algorithm is validated by experiments over both simulated and real-world social networks. The method can be also used to further improve the accessibility of anonymous and anti-censorship systems. [Copyright &y& Elsevier]

Abstract: A new PID controller for resistant differential control against load disturbance is introduced that can be used for load frequency control (LFC) application. Parameters of the controller have been specified by using imperialist competitive algorithm (ICA). Load disturbance, which is due to continuous and rapid changes of small loads, is always a problem for load frequency control of power systems. This paper introduces a new method to overcome this problem that is based on filtering technique which eliminates the effect of this kind of disturbance. The object is frequency regulation in each area of the power system and decreasing of power transfer between control areas, so the parameters of the proposed controller have been specified in a wide range of load changes by means of ICA to achieve the best dynamic response of frequency. To evaluate the effectiveness of the proposed controller, a three-area power system is simulated in MATLAB/SIMULINK. Each area has different generation units, so utilizes controllers with different parameters. Finally a comparison between the proposed controller and two other prevalent PI controllers, optimized by GA and Neural Networks, has been done which represents advantages of this controller over others. [Copyright &y& Elsevier]

Abstract: This paper presents a multi-robot simultaneous localization and map building (SLAM) algorithm, suitable for environments which can be represented in terms of lines and segments. Linear features are described by adopting the recently introduced -Space representation, which provides a unified framework for the parameterization of different kinds of features. The proposed solution to the cooperative SLAM problem is split into three phases. Initially, each robot solves the SLAM problem independently. When two robots meet, their local maps are merged together using robot-to-robot relative range and bearing measurements. Then, each robot starts over with the single-robot SLAM algorithm, by exploiting the merged map. The proposed map fusion technique is specifically tailored to the adopted feature representation, and takes into account explicitly the uncertainty affecting both the maps and the robot mutual measurements. Numerical simulations and experiments with a team composed of two robots performing SLAM in a real-world scenario, are presented to evaluate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Unstructured meshes are in widespread use throughout computational physics, but calculating diagnostics of simulations on such meshes can be challenging. For example, in geophysical fluid dynamics, it is frequently desirable to compute directional integrals such as vertical integrals and zonal averages; however, it is difficult to compute these on meshes with no inherent spatial structure. This is widely regarded as an obstacle to the adoption of unstructured mesh numerical modelling in this field. In this paper, we describe an algorithm by which one can exactly compute such directional integrals on arbitrarily unstructured meshes. This is achieved via the solution of a problem of computational geometry, constructing the supermesh of two meshes. We demonstrate the utility of this approach by applying it to a classical geophysical fluid dynamics system: the thermally driven rotating annulus. This addresses an important objection to the more widespread use of unstructured mesh modelling. [Copyright &y& Elsevier]

Abstract: The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts with the constraint programming principles developed within the artificial intelligence community. While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach. The emphasis lies on developing high-level modeling languages and general solvers that specify what the problem is, rather than outlining how a solution should be computed, yet are powerful enough to be used across a wide variety of applications and application domains. This paper contributes a declarative constraint programming approach to data mining. More specifically, we show that it is possible to employ off-the-shelf constraint programming techniques for modeling and solving a wide variety of constraint-based itemset mining tasks, such as frequent, closed, discriminative, and cost-based itemset mining. In particular, we develop a basic constraint programming model for specifying frequent itemsets and show that this model can easily be extended to realize the other settings. This contrasts with typical procedural data mining systems where the underlying procedures need to be modified in order to accommodate new types of constraint, or novel combinations thereof. Even though the performance of state-of-the-art data mining systems outperforms that of the constraint programming approach on some standard tasks, we also show that there exist problems where the constraint programming approach leads to significant performance improvements over state-of-the-art methods in data mining and as well as to new insights into the underlying data mining problems. Many such insights can be obtained by relating the underlying search algorithms of data mining and constraint programming systems to one another. We discuss a number of interesting new research questions and challenges raised by the declarative constraint programming approach to data mining. [Copyright &y& Elsevier]

Abstract: AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically. [Copyright &y& Elsevier]

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking. [ABSTRACT FROM AUTHOR]

Feature selection is a common dimensionality reduction technique of fundamental importance in big data. A common approach for reducing the running time of feature selection is to perform it in two stages. In the first stage a fast and simple filter is applied to select good candidates. The number of candidates is further reduced in the second stage by an accurate algorithm that may run significantly slower. There are two main variants of feature selection: unsupervised and supervised. In the supervised variant features are selected for predicting labels, while the unsupervised variant does not use labels at all. We describe a general framework that can use an arbitrary off-the-shelf unsupervised algorithm for the second stage. The algorithm is applied to the selection obtained in the first stage weighted appropriately. Our main technical result is a method for calculating weights for the columns that need to be selected in the second stage. We show that these weights can be computed as the solution to a constrained quadratic optimization problem. The solution is deterministic, and improves on previously published studies that use probabilistic ideas to compute similar weights. To the best of our knowledge our approach is the first technique for converting a supervised feature selection problem into an unsupervised problem. Complexity analysis shows that the proposed technique is very fast, can be implemented in a single pass over the data, and can take advantage of data sparsity. Experimental results show that the accuracy of the proposed method is comparable to that of much slower techniques. [ABSTRACT FROM AUTHOR]

Currently, the path planning problem is one of the most researched topics in autonomous robotics. That is why finding a safe path in a cluttered environment for a mobile robot is an important requirement for the success of any such mobile robot project. In this work, a developed algorithm based on free segments and a turning point strategy for solving the problem of robot path planning in a static environment is presented. The aim of the turning point approach is to search a safe path for the mobile robot, to make the robot moving from a starting position to a destination position without hitting obstacles. This proposed algorithm handles two different objectives which are the path safety and the path length. In addition, a robust control law which is called sliding mode control is proposed to control the stabilization of an autonomous mobile robot to track a desired trajectory. Finally, simulation results show that the developed approach is a good alternative to obtain the adequate path and demonstrate the efficiency of the proposed control law for robust tracking of the mobile robot. [ABSTRACT FROM AUTHOR]

The purpose of this work is to estimate target coordinates and velocity. Target tracking is performed by an autonomous underwater vehicle (AUV). The observer is equipped with the direction finder that measures the azimuth and elevation angles of the target and the target range. Such problems are normally solved with the aid of non-linear filters due to nonlinearity of measurements. However, most filters produce estimations with unknown bias in the absence of estimations of accuracy. An important problem is related to unobservability of the target. A pseudomeasurement method is proposed to transform the problem of estimation into a linear problem. Such an approach makes possible to estimate coordinates and velocity of a moving target and solve the problem of unobservability. Results of computer simulation prove efficiency of the proposed algorithm in the problem of passive target tracking when only angles are measured. [ABSTRACT FROM AUTHOR]

Given a set of events and a set of robots, the dispatch problem is to allocate one robot for each event to visit it. In a single round, each robot may be allowed to visit only one event (matching dispatch), or several events in a sequence (sequence dispatch). In a distributed setting, each event is discovered by a sensor and reported to a robot. Here, we present novel algorithms aimed at overcoming the shortcomings of several existing solutions. We propose pairwise distance based matching algorithm (PDM) to eliminate long edges by pairwise exchanges between matching pairs. Our sequence dispatch algorithm (SQD) iteratively finds the closest event-robot pair, includes the event in dispatch schedule of the selected robot and updates its position accordingly. When event-robot distances are multiplied by robot resistance (inverse of the remaining energy), the corresponding energy-balanced variants are obtained. We also present generalizations which handle multiple visits and timing constraints. Our localized algorithm MAD is based on information mesh infrastructure and local auctions within the robot network for obtaining the optimal dispatch schedule for each robot. The simulations conducted confirm the advantages of our algorithms over other existing solutions in terms of average robot-event distance and lifetime. [ABSTRACT FROM PUBLISHER]

What is called numerical reproducibility is the problem of getting the same result when the scientific computation is run several times, either on the same machine or on different machines, with different types and numbers of processing units, execution environments, computational loads, etc. This problem is especially stringent for HPC numerical simulations. In what follows, we identify the problems encountered when implementing interval routines in floating-point arithmetic. Some are well-known and common in numerical computations, some are specific to interval computations. We propose here a classification of floating-point issues by distinguishing their severity with respect to correctness and tightness of the computed interval result. In fact, interval computation can accommodate the lack of numerical reproducibility as long as it does not affect the inclusion property, which is the main property of interval arithmetic. Several ways to preserve the inclusion property are presented, on the example of the product of matrices with interval coefficients. [ABSTRACT FROM AUTHOR]

We present a theory, and its computer implementation, of how mental simulations underlie the abductions of informal algorithms and deductions from these algorithms. Three experiments tested the theory's predictions, using an environment of a single railway track and a siding. This environment is akin to a universal Turing machine, but it is simple enough for nonprogrammers to use. Participants solved problems that required use of the siding to rearrange the order of cars in a train (experiment 1). Participants abduced and described in their own words algorithms that solved such problems for trains of any length, and, as the use of simulation predicts, they favored "while-loops" over "for-loops" in their descriptions (experiment 2). Given descriptions of loops of procedures, participants deduced the consequences for given trains of six cars, doing so without access to the railway environment (experiment 3). As the theory predicts, difficulty in rearranging trains depends on the numbers of moves and cars to be moved, whereas in formulating an algorithm and deducing its consequences, it depends on the Kolmogorov complexity of the algorithm. Overall, the results corroborated the use of a kinematic mental model in creating and testing informal algorithms and showed that individuals differ reliably in the ability to carry out these tasks. [ABSTRACT FROM AUTHOR]

A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms. [ABSTRACT FROM AUTHOR]

We consider the augmented Lagrangian method (ALM) as a solver for the fused lasso signal approximator (FLSA) problem. The ALM is a dual method in which squares of the constraint functions are added as penalties to the Lagrangian. In order to apply this method to FLSA, two types of auxiliary variables are introduced to transform the original unconstrained minimization problem into a linearly constrained minimization problem. Each updating in this iterative algorithm consists of just a simple one-dimensional convex programming problem, with closed form solution in many cases. While the existing literature mostly focused on the quadratic loss function, our algorithm can be easily implemented for general convex loss. We also provide some convergence analysis of the algorithm. Finally, the method is illustrated with some simulation datasets. [ABSTRACT FROM AUTHOR]

We present a new generic sequential importance sampling algorithm, called stochastic enumeration (SE) for counting #P-complete problems, such as the number of satisfiability assignments and the number of perfect matchings (permanent). We show that SE presents a natural generalization of the classic one-step-look-ahead algorithm in the sense that it: Runs in parallel multiple trajectories instead of a single one; Employs a polynomial time decision making oracle, which can be viewed as an n-step-look-ahead algorithm, where n is the size of the problem. Our simulation studies indicate good performance of SE as compared with the well-known splitting and SampleSearch methods. [ABSTRACT FROM AUTHOR]

The authors present finite difference algorithms for solving nonstationary problems on dynamics of elastic media and structures, and perform numerical modeling of the following applied problems: stroke on an elastic half-plane by a rigid die; shock piling; generation of a pendulum wave in a blocky medium by a local impulse effect. One of the goals of the presented modeling is driving the problem solutions with minimized influence of spurious effects inherent to numerical approximation. Finally, the authors compare the results obtained in different models of elastic media. [ABSTRACT FROM AUTHOR]

In fringe projection profilometry (FPP), non-sinusoidal fringes due to the gamma effect of a projector will cause measurement errors. To solve this problem, a binary defocusing technique has been introduced in recent years, yet the appropriate defocusing is hard to evaluate quantitatively. An innovative approach to quantify binary defocusing in real-time is proposed. No matter how the projector and camera are arranged in FPP system, the fringe period in a captured defocusing fringe image is strictly varying; therefore, unlike previous methods, the proposed method uses a limited window for defocusing evaluation. This not only improves the accuracy of defocusing evaluation, but also greatly improves the evaluation speed, thus in turn enabling real-time evaluation. In addition, numerical differentiation is introduced on the window using a modified five-interval-point algorithm to effectively improve the sensitivity to improper (inadequate or excessive) defocusing. The difference between the numerical differentiation and its fundamental harmonic extracted by Levenberg–Marquardt iterative is taken as an evaluation value of binary defocusing. The smaller the difference, the more appropriate is the defocusing. By minimizing this difference, the most appropriate defocusing can be obtained quantitatively. Both numerical simulations and experiments validate the high sensitivity and high speed of the proposed method. [ABSTRACT FROM AUTHOR]

A theory of equivalent integer programs is developed that shows that every all-integer integer programming problem is equivalent to infinitely many other integer programming problems. The equivalence is such that the solution to any one problem in the equivalence class determines the solution to every other problem in the class. Procedures to construct certain canonical problems in each equivalence class are described. The relationship of this theory to computational algorithms is discussed. [ABSTRACT FROM AUTHOR]

In this report, twenty-nine integer linear programming problems, ranging from very simple to apparently rather difficult, are introduced. The results of running these problems on four different computer codes, involving five distinct algorithms, are presented as both time and iteration data. A brief description of the codes used is also included. [ABSTRACT FROM AUTHOR]