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This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a whole body control framework for the control of a dual arm underwater vehicle manipulator system developed in the context of the MARIS Italian research project, which deals with the control and coordination of underwater vehicles for manipulation and transportation problems. The proposed framework is the extension of the one used in the successful TRIDENT FP7 project that has been improved to be able to deal with multidimensional inequality control objectives. After the presentation of the mathematical background, the paper presents some simulation results showing the good performances of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies the distributed average tracking problem for multiple time-varying signals generated by linear dynamics, whose reference inputs are nonzero and not available to any agent in the network. In the edge-based framework, a pair of continuous algorithms with, respectively, static and adaptive coupling strengths is designed. Based on the boundary layer concept, the proposed continuous algorithm with static coupling strengths can asymptotically track the average of multiple reference signals without the chattering phenomenon. Furthermore, for the case of algorithms with adaptive coupling strengths, average tracking errors are uniformly ultimately bounded and exponentially converge to a small adjustable bounded set. Finally, a simulation example is presented to show the validity of theoretical results. [ABSTRACT FROM AUTHOR]

With a small number of elements in uniform circular arrays (UCAs), the performance of beamspace transformation (BT) based methods for 2D direction-of-arrival (DOA) estimation in the presence of mutual coupling suffers serious degradation. The performance breakdown is associated with the residual term which is introduced by the BT. To solve this problem, a novel algorithm which modifies the beamspace data is proposed in this paper. Based on the estimated azimuth angles obtained by a UCA-RARE-like method and the estimated elevation angles obtained by a new searching method, the beamspace data is iteratively revised by removing its residual component. Thus, unbiased 2D DOA estimates can be obtained depending on the revised beamspace data. Simulation results are given to verify the effectiveness of the proposed algorithm and a comparison is made with results from existed methods. [ABSTRACT FROM AUTHOR]

This paper presents a new Electromagnetism-like Mechanism (EM) algorithm with Split, Probe and Compare feature (SPC-EM). The proposed algorithm replaces the local search segment of a standard EM with a new search scheme named Split, Probe, and Compare (SPC). A nonlinear equation is designed to systematically and dynamically adjust the length of the probes based on the outcome of the Compare segment in each iteration. Extensive computational simulations and comparisons on 10 different benchmark problems from the literature were carried out. Results show that the new modified mechanism outperformed all other algorithms involved in the benchmarking. We thus conclude that the proposed SPC-EM works well with the designed probe-length tuning equation in solving numerical optimization problems. [ABSTRACT FROM AUTHOR]

Abstract: Computer simulation is an increasingly popular tool for determining the most suitable hybrid energy system type, design and control for an isolated community or a cluster of villages. This paper presents the development of the optimum control algorithm based on combined dispatch strategies, to achieve the optimal cost of battery incorporated hybrid energy system for electricity generation, during a period of time by solving the mathematical model, which was developed in Part I of this tri-series paper. The main purpose of the control system proposed here is to reduce, as much as possible, the participation of the diesel generator in the electricity generation process, taking the maximum advantage of the renewable energy resources available. The overall load dispatch scenario is controlled by the availability of renewable power, total system load demand, diesel generator operational constraints and the proper management of the battery bank. The incorporation of a battery bank makes the control operation more practical and relatively easier. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents’ states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms. [ABSTRACT FROM AUTHOR]

A novel attitude algorithm for fiber strapdown inertial navigation system (SINS) is studied in this paper. The noncommutativity rotation is one of the major errors in attitude updating for SINS. As a general rule, accuracy can be improved by increasing attitude updating frequency. However, it is limited by gyro and computer technology. To solve this problem, a modified algorithm for fiber SINS is proposed. This algorithm uses angular rate as algorithm input and estimates the noncommutativity error with the cross product of angular rates and the cross product of previous accumulated gyro outputs with current gyro outputs. Simulations and experiments are performed under coning motion and three-axis turntable environment, and the results show that the accuracy of the proposed algorithm can be improved effectively under coning motion, as well as in three-axis turntable environment, though not as greatly. [ABSTRACT FROM AUTHOR]

The long term of this ongoing research has to do with increasing degree of autonomy for robots involved in underwater intervention missions. Bearing in mind that the specific mission to face has been the intervention on a panel, in this paper some results in different development stages are presented by using the real mechatronics and the panel mockup. Furthermore, some details are highlighted describing two methodologies implemented for the required visually-guided manipulation algorithms. These algorithms are used to correct problems caused by a bad initialization or miscalibration of the arm. They consist of detecting a marker placed in a known position of the arm and updating the value of each joint after any detection. A roadmap explaining the different testbeds used for experimental validation, in increasing complexity order, is also presented. It is worth mentioning that the aforementioned results would be impossible without previously generated know-how for both the complete developed mechatronics for the autonomous underwater vehicle for intervention, and the required 3D simulation tool. In summary, thanks to the implemented approach, the intervention system is able to control the way in which the gripper approximates and manipulates the two panel devices (i.e. a valve and a connector) in an autonomous manner. Results in different scenarios demonstrate the reliability and feasibility of this autonomous intervention system in water tank and pool conditions. [ABSTRACT FROM AUTHOR]

In the clutter environments, persistently tracking multiple objects is still a very challenging problem. In this paper, an A * algorithm with dynamic weights is developed to solve this problem. Firstly, the problem of multiple object tracking is considered as an integer programming of flow network model. Based on this model, afterward, we relax the problem of integer programming problem to a standard linear programming and achieve the global optimal solution with the proposed algorithm, which has been proven in this work. Compared with other advanced methods employed in the modern complex environments, the proposed method is on the merit of lower calculation complexity and better tracking accuracy and robustness. Finally, the simulation result is demonstrated, which reveals that the proposed algorithm saves the time costs vastly. [ABSTRACT FROM AUTHOR]

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]

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]

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: 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: 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]