Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Equation oriented method for Rectisol wash modeling and analysis

Rectisol process is more efficient in comparison with other physical or chemical absorption methods for gas purification. To implement a real time simulation of Rectisol process, thermodynamic model and simulation strategy are needed. In this paper, a method of modified statistical associated fluid theory with perturbation theory is used to predict thermodynamic behavior of process. As Rectisol process is a highly heat-integrated process with many loops, a method of equation oriented strategy, sequential quadratic programming, is used as the solver and the process converges perfectly. Then analyses are conducted with this simulator.

Fuzzy linear model for production optimization of mining systems with multiple entities

Planning and production optimization within multiple mines or several work sites （entities） mining systems by using fuzzy linear programming （LP） was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

Influence of laser pulse on the autocorrelation function of H in a strong electric field

The autocorrelation function of electronic wave packet of hydrogen atom in a strong electric field below the zero-field ionization threshold is investigated in the formalism of semiclassical theory. It is found that the autocorrelation depends on the applied laser pulse significantly. In the case of narrow laser pulse, the reviving peaks in the autocorrelation can be attributed to the closed orbits of electrons, which are related to the classical dynamics of the system. But this correspondence is wiped out with increasing the laser width because of the interference among the adjacent reviving peaks.

An improved partial SPIHT with classified weighted rate-distortion optimization for interferential multispectral image compression

Based on the property analysis of interferential multispectral images, a novel compression algorithm of partial set partitioning in hierarchical trees （SPIHT） with classified weighted rate-distortion optimization is presented. After wavelet decomposition, partial SPIHT is applied to each zero tree independently by adaptively selecting one of three coding modes according to the probability of the significant coefficients in each bitplane. Meanwhile the interferential multispectral image is partitioned into two kinds of regions in terms of luminous intensity, and the rate-distortion slopes of zero trees are then lifted with classified weights according to their distortion contribution to the constructed spectrum. Finally a global rate- distortion optimization truncation is performed. Compared with the conventional methods, the proposed algorithm not only improves the performance in spatial domain but also reduces the distortion in spectral domain.

Receding horizon control for cooperative search of multi-UAVs based on differential evolution

Purpose–The purpose of this paper is to present a hybrid method of intelligent optimization algorithm and Receding Horizon Control.The method is applied to solve the problem of cooperative search of multi-unmanned aerial vehicles(multi-UAVs).Design/methodology/approach–The intelligent optimization of Differential Evolution(DE)makes the complex problem of multi-UAVs cooperative search a regular function optimization problem.To meet the real-time requirement,the idea of Receding Horizon Control is applied.An Extended Search Map based on hormone information is used to describe the uncertain environment information.Findings–Simulation results indicate effectiveness of the hybrid method in solving the problem of cooperative search for multi-UAVs.Originality/value–The paper presents an interesting hybrid method of DE and Receding Horizon Control for the problem of cooperative multi-UAVs.

Equidistance target-following controller for underactuated autonomous underwater vehicles

Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms.The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target.Furthermore,the distance at which the AUV follows the target is constrained,reducing the probability of detection and unwanted behavior change of the target.Design/methodology/approach–First,a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind.Using a Lyapunov based method the stability of the kinematic controller is proven.Second,a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.Findings–The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle.Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.Originality/value–The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane.The method allows control of both the direction of approach and the distance from a moving object.

A UTP semantic model for Orc language with execution status and fault handling

The Orc language is a concurrency calculus pro- posed to study the orchestration patterns in service oriented computing. Its special features, such as high concurrency and asynchronism make it a brilliant subject for studying web applications that rely on web services. The conventional se- mantics for Orc does not contain the execution status of ser- vices so that a program cannot determine whether a service has terminated normally or halted with a failure after it pub- lished some results. It means that this kind of failure cannot be captured by the fault handler. Furthermore, such a seman- tic model cannot establish an order saying that a program is better if it fails less often. This paper employs UTP methods to propose a denotational semantic model for Orc that con- rains execution status information. A failure handling seman- tics is defined to recover a failure execution back to normal. A refinement order is defined to compare two systems based on their execution failures. Based on this order, a system that introduces a failure recovery mechanism is considered bet- ter than one without. An extended operational semantics is also proposed and proven to be equivalent to the denotational semantics.

Algorithm fusion to improve detection of lung cancer on chest radiographs

Purpose-The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer.Various image processing algorithms are presented for different types of lesions,and a scheme is proposed for the combination of results.Design/methodology/approach-A computer aided detection(CAD)scheme was developed for detection of lung cancer.It enables different lesion enhancer algorithms,sensitive to specific lesion subtypes,to be used simultaneously.Three image processing algorithms are presented for the detection of small nodules,large ones,and infiltrated areas.The outputs are merged,the false detection rate is reduced with four separated support vector machine(SVM)classifiers.The classifier input comes from a feature selection algorithm selecting from various textural and geometric features.A total of 761 images were used for testing,including the database of the Japanese Society of Radiological Technology(JSRT).Findings-The fusion of algorithms reduced false positives on average by 0.6 per image,while the sensitivity remained 80 per cent.On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image.The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured.The system was compared to other published methods.Originality/value-The study described in the paper proves the usefulness of lesion enhancement decomposition,while proposing a scheme for the fusion of algorithms.Furthermore,a new algorithm is introduced for the detection of infiltrated areas,possible signs of lung cancer,neglected by previous solutions.

H-infinity control for air-breathing hypersonic vehicle based on online simultaneous policy update algorithm

Purpose–The air-breathing hypersonic vehicle(AHV)includes intricate inherent coupling between the propulsion system and the airframe dynamics,which results in an intractable nonlinear system for the controller design.The purpose of this paper is to propose an H1 control method for AHV based on the online simultaneous policy update algorithm(SPUA).Design/methodology/approach–Initially,the H1 state feedback control problem of the AHV is converted to the problem of solving the Hamilton-Jacobi-Isaacs(HJI)equation,which is notoriously difficult to solve both numerically and analytically.To overcome this difficulty,the online SPUA is introduced to solve the HJI equation without requiring the accurate knowledge of the internal system dynamics.Subsequently,the online SPUA is implemented on the basis of an actor-critic structure,in which neural network(NN)is employed for approximating the cost function and a least-square method is used to calculate the NN weight parameters.Findings–Simulation study on the AHV demonstrates the effectiveness of the proposed H1 control method.Originality/value–The paper presents an interesting method for the H1 state feedback control design problem of the AHV based on online SPUA.

Trace Semantics and Algebraic Laws for Total Store Order Memory Model

Modern multiprocessors deploy a variety of weak memory models(WMMs).Total Store Order(TSO)is a widely-used weak memory model in SPARC implementations and x86 architecture.It omits the store-load constraint by allowing each core to employ a write buffer.In this paper,we apply Unifying Theories of Programming(abbreviated as UTP)in investigating the trace semantics for TSO,acting in the denotational semantics style.A trace is expressed as a sequence of snapshots,which records the changes in registers,write buffers and the shared memory.All the valid execution results containing reorderings can be described after kicking out those that do not satisfy program order and modification order.This paper also presents a set of algebraic laws for TSO.We study the concept of head normal form,and every program can be expressed in the head normal form of the guarded choice which is able to model the execution of a program with reorderings.Then the linearizability of the TSO model is supported.Furthermore,we consider the linking between trace semantics and algebraic semantics.The linking is achieved through deriving trace semantics from algebraic semantics,and the derivation strategy under the TSO model is provided.

A New Computational Approach for Solving Optimal Control of Linear PDEs Problem

In this paper, we present a new computational approach for solving an internal optimal control problem, which is governed by a linear parabolic partial differential equation. Our approach is to approximate the PDE problem by a nonhomogeneous ordinary differential equation system in higher dimension. Then, the homogeneous part of ODES is solved using semigroup theory. In the next step, the convergence of this approach is verified by means of Toeplitz matrix. In the rest of the paper, the optimal control problem is solved by utilizing the solution of homogeneous part. Finally, a numerical example is given.

A version of Geiringer-like theorem for decision making in the environments with randomness and incomplete information

Purpose-The purpose of this paper is to establish a version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte-Carlo sampling algorithms that provably increase the AI potential.Design/methodology/approach-In the current paper the authors set up a mathematical framework,state and prove a version of a Geiringer-like theorem that is very well-suited for the development of Mote-Carlo sampling algorithms to cope with randomness and incomplete information to make decisions.Findings-This work establishes an important theoretical link between classical population genetics,evolutionary computation theory and model free reinforcement learning methodology.Not only may the theory explain the success of the currently existing Monte-Carlo tree sampling methodology,but it also leads to the development of novel Monte-Carlo sampling techniques guided by rigorous mathematical foundation.Practical implications-The theoretical foundations established in the current work provide guidance for the design of powerful Monte-Carlo sampling algorithms in model free reinforcement learning,to tackle numerous problems in computational intelligence.Originality/value-Establishing a Geiringer-like theorem with non-homologous recombination was a long-standing open problem in evolutionary computation theory.Apart from overcoming this challenge,in a mathematically elegant fashion and establishing a rather general and powerful version of the theorem,this work leads directly to the development of novel provably powerful algorithms for decision making in the environment involving randomness,hidden or incomplete information.