An efficient trust estimation model for multi-agent systems using temporal difference learning

In multi-agent system (MAS) applications, teamwork among the agents is essential as the agents are required to collaborate and pool resources to execute the given tasks and complete the objectives successfully. A vital part of the collaboration is sharing of information and resources in order to optimize their efforts in achieving the given objectives. Under such collaborative environment, trust among the agents plays a critical role to ensure efficient cooperation. This study looks into developing a trust evaluation model that can empirically evaluate the trust of one agent on the other. The proposed model is developed using temporal difference learning method, incorporating experience gained through interactions into trust evaluation. Simulation experiments are conducted to evaluate the performance of the developed model against some of the most recent models reported in the literature. The results of the simulation experiments indicate that the proposed model performs better than the comparison models in estimating trust more effectively.

     

HySAT: An efficient proof engine for bounded model checking of hybrid systems

In this paper we present HySAT, a bounded model checker for linear hybrid systems, incorporating a tight integration of a DPLL–based pseudo–Boolean SAT solver and a linear programming routine as core engine. In contrast to related tools like MathSAT, ICS, or CVC, our tool exploits the various optimizations that arise naturally in the bounded model checking context, e.g.isomorphic replication of learned conflict clauses or tailored decision strategies, and extends them to the hybrid domain. We demonstrate that those optimizations are crucial to the performance of the tool.     

Extension of the divide-and-conquer algorithm for the efficient inverse dynamics analysis of multibody systems

This paper presents a new mathematical framework to extend the Generalized Divide-and-Conquer Algorithm (GDCA) for the inverse dynamics analysis of fully actuated constrained multibody systems. Inverse-GDCA (iGDCA) is a highly parallelizable method which does not create the mass and Jacobian matrices of the entire system. In this technique, generalized driving forces and constraint loads due to kinematic pairs are clearly and separately differentiated from each other in the equations of motion. As such, it can be easily used for control scheme purposes. iGDCA works based on a series of recursive assembly and disassembly passes to form and solve the equations governing the inverse dynamics of the system. Herein, the mathematical formulations to efficiently combine the dynamics of consecutive bodies in the assembly pass for the purpose of inverse dynamics analysis are presented. This is followed by generating the disassembly pass algorithm to efficiently compute generalized actuating forces. Furthermore, this paper presents necessary mathematical formulations to efficiently treat the inverse dynamics of multibody systems involving kinematic loops with various active and passive boundary conditions. This is followed by the design of a new strategy to efficiently perform the assembly–disassembly pass in these complex systems while avoiding unnecessary computations. Finally, the presented method is applied to selected open-chain and closed-chain multibody systems.     

An efficient video-on-demand model

An efficient video-on-demand system uses a practical, technologically sophisticated model to serve the viewing needs of a wide audience, including meeting the peak demand for popular, newly released films     

An efficient mutual authentication scheme for IoT systems

The Journal of Supercomputing - The resource-constrained nature of Internet of Things (IoT) devices and their diversity and abundancy represent a major challenge for the development of efficient...     

An efficient raster font compression for embedded systems

This paper presents a new lossless raster font compression method that uses vertex chain code to define character’s outline. Obtained chain codes are compressed by Huffman coding algorithm. The results show that the new method requires least memory space to store the raster fonts among the known methods. Moreover, the font size has almost no impact on the coder efficiency. Due to the low complexity of the decoder that occupies only 2.7 kB of memory space, this method is ideal for use in embedded systems.     

An efficient partitioning algorithm for distributed virtualenvironment systems

Distributed virtual environment (DVE) systems model and simulate the activities of thousands of entities interacting in a virtual world over a wide area network. Possible applications for DVE systems are multiplayer video games, military and industrial trainings, and collaborative engineering. In general, a DVE system is composed of many servers and each server is responsible to manage multiple clients who want to participate in the virtual world. Each server receives updates from different clients (such as the current position and orientation of each client) and then delivers this information to other clients in the virtual world. The server also needs to perform other tasks, such as object collision detection and synchronization control. A large scale DVE system needs to support many clients and this imposes a heavy requirement on networking resources and computational resources. Therefore, how to meet the growing requirement of bandwidth and computational resources is one of the major challenges in designing a scalable and cost-effective DVE system. In this paper, we propose an efficient partitioning algorithm that addresses the scalability issue of designing a large scale DVE system. The main idea is to dynamically divide the virtual world into different partitions and then efficiently assign these partitions to different servers. This way, each server will process approximately the same amount of workload. Another objective of the partitioning algorithm is to reduce the server-to-server communication overhead. The theoretical foundation of our dynamic partitioning algorithm is based on the linear optimization principle. We also illustrate how one can parallelize the proposed partitioning algorithm so that it can efficiently partition a very large scale DVE system. Lastly, experiments are carried out to illustrate the effectiveness of the proposed partitioning algorithm under various settings of the virtual world     

An efficient dynamic model for batch distillation

A novel, rigorous and efficient solution technique for multicomponent batch distillation modelling equations is proposed. Model predictions using the technique are shown to be in close agreement with experimental batch distillation data for a ten sieve tray, 15 cm diameter column separating ethanol and water. The results also show improved accuracy over commercially available programs for batch distillation. The method incorporates rigorous dynamic energy blances as well as accurate representation of both tray hydraulics and non-ideal mass transfer. The technique is based on a functional approximation for liquid enthalpy and makes a difficult-to-calculate temperature derivative implicit in other terms in the equations, eliminating the need for iterative solution techniques. The numerical efficiency of the method permits its utilization in model-based optimization and control calculations. The modelling approach is applicable to both batch and continuous dynamic distillation models.     

An efficient dynamic scheduling algorithm for multiprocessorreal-time systems

Many time-critical applications require predictable performance and tasks in these applications have deadlines to be met. In this paper, we propose an efficient algorithm for nonpreemptive scheduling of dynamically arriving real-time tasks (aperiodic tasks) in multiprocessor systems. A real-time task is characterized by its deadline, resource requirements, and worst case computation time on p processors, where p is the degree of parallelization of the task. We use this parallelism in tasks to meet their deadlines and, thus, obtain better schedulability compared to nonparallelizable task scheduling algorithms. To study the effectiveness of the proposed scheduling algorithm, we have conducted extensive simulation studies and compared its performance with the myopic scheduling algorithm. The simulation studies show that the schedulability of the proposed algorithm is always higher than that of the myopic algorithm for a wide variety of task parameters     

An efficient algorithm for the tensor product model transformation

The tensor-product (TP) model transformation was proposed recently as a numerical and automatically executable method which is capable of transforming linear parameter varying (LPV) state-space models into the higher order singular value decomposition (HOSVD) based canonical form of polytopic models. The crucial disadvantage of the TP model transformation is that its computational load explodes with the density of discretization and the dimensionality of the parameter vector of the parameter-varying state-space model. In this paper we propose a new algorithm that leads to considerable reduction of the computation in the TP model transformation. The main idea behind the modified algorithm is to minimize the number of discretized points to acquire as much information as possible. The modified TP model transformation can readily be executed on a regular computer efficiently and concisely, especially in higher dimensional cases when the original TP model transformation fails. The paper also presents numerical examples to show the effectiveness of the new algorithm.     

An efficient and secure authentication protocol for RFID systems

The use of radio frequency identification (RFID) tags may cause privacy violation of users carrying an RFID tag. Due to the unique identification number of the RFID tag, the possible privacy threats are information leakage of a tag, traceability of the consumer, denial of service attack, replay attack and impersonation of a tag, etc. There are a number of challenges in providing privacy and security in the RFID tag due to the limited computation, storage and communication ability of low-cost RFID tags. Many research works have already been conducted using hash functions and pseudorandom numbers. As the same random number can recur many times, the adversary can use the response derived from the same random number for replay attack and it can cause a break in location privacy. This paper proposes an RFID authentication protocol using a static identifier, a monotonically increasing timestamp, a tag side random number and a hash function to protect the RFID system from adversary attacks. The proposed protocol also indicates that it requires less storage and computation than previous existing RFID authentication protocols but offers a larger range of security protection. A simulation is also conducted to verify some of the privacy and security properties of the proposed protocol.     

An efficient weighted bi-objective scheduling algorithm for heterogeneous systems

This paper proposes the Makespan and Reliability Cost Driven (MRCD) heuristic, a static scheduling strategy for heterogeneous distributed systems that not only minimizes the makespan, but also maximizes the reliability of the application. The MRCD scheduling decisions are guided by a weighted function that considers both objectives simultaneously, instead of prioritizing one of them. This work also introduces a classification of the solutions produced by weighted bi-objective schedulers to aid users to tune the weighting function such that an appropriate solution can be selected in accordance with their needs. In comparison with the related work, MRCD produced schedules with makespans that were significantly better then those produced by the other strategies at expense of an insignificant deterioration in reliability.     

An efficient fixed-lag smoothing algorithm for discrete linear systems

The modified Bryson-Frazier fixed interval smoothing algorithm [6], is an addendem to the Kalman filter. This algorithm when applied to the problem of fixed-lag smoothing is computationally more efficient than the algorithms recently reported in refs. [1–3]. Features of the algorithm are ease of implentation, computational efficiency, reduced storage requirements, and stability.     

An efficient chemical systems modelling approach

Systems of stiff chemical reactions are often associated with atmospheric chemistry modelling, which plays a very important role in the studies of stratospheric ozone depletion, tropospheric air pollution problems, and future chemistry-climate feedbacks and interactions. This paper revisits an open-source stiff system solver SVODE and presents its efficient use in modelling different levels of complexity of a range of chemical systems. The chemical systems discussed here are the Lotka–Volterra (predator–prey) model, the Brusselator model, the Oregonator model, and the Lorenz model. The first two models consist of two variables, while the remaining two models consist of three variables. Finally, an application of this modelling approach to a generalised organic/NOx mechanism for characterising air pollution development is presented. Since the SVODE is an open-source code, and the simulations were run on a Linux PC (with g77 compiler), all results discussed in this paper can be easily reproduced. Most importantly, the approach shown here can be readily extended to other larger scale applications such as the three-dimensional air pollution modelling.     

An efficient object promotion algorithm for persistent object systems

We report on a bulk object‐loading algorithm for persistent object stores called Ghosted Allocation. It allocates large numbers of objects in a persistent store atomically, efficiently, and reliably. Its main strengths are that it minimizes I/O traffic, optimizes the disk access pattern, and does not impose complex requirements on applications. Our experiments demonstrate that the Ghosted Allocation algorithm is efficient and most importantly scalable, sustaining allocation rates of up to 63 000 objects s^‐1. Copyright © 2001 John Wiley & Sons, Ltd.     

An efficient algebraic multigrid method for solving optimality systems

An algebraic multigrid method (AMG) for solving convection-diffusion optimality systems is presented. Results of numerical experiments demonstrate robustness of the AMG scheme with respect to changes of the weight of the cost of the control and show that the computational performance of the proposed AMG scheme is comparable to that of AMG applied to single scalar equations.     

An efficient iterated method for mathematical biology model

The purpose of this study is to introduce an efficient iterated homotopy perturbation transform method (IHPTM) for solving a mathematical model of HIV infection of CD4+ T cells. The equations are Laplace transformed, and the nonlinear terms are represented by He’s polynomials. The solutions are obtained in the form of rapidly convergent series with elegantly computable terms. This approach, in contrast to classical perturbation techniques, is valid even for systems without any small/large parameters and therefore can be applied more widely than traditional perturbation techniques, especially when there do not exist any small/large quantities. A good agreement of the novel method solution with the existing solutions is presented graphically and in tabulated forms to study the efficiency and accuracy of IHPTM. This study demonstrates the general validity and the great potential of the IHPTM for solving strongly nonlinear problems.