Double Layer ACO Algorithm for the Multi-Objective FJSSP

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. In this work, a double layer Ant Colony Optimization (ACO) algorithm is proposed for the Flexible Job Shop Scheduling Problem (FJSSP). In the proposed algorithm, two different ACO algorithms are applied to solve the FJSSP with a hierarchical way. The primary mission of upper layer ACO algorithm is achieving an excellent assignment of operations to machines. The leading task of lower layer ACO algorithm is obtaining the optimal sequencing of operations on each machine. Experimental results suggest that the proposed algorithm is a feasible and effective approach for the multi-objective FJSSP.

Optimal Control of Production Processes with Variable Execution Times

The paper deals with the optimal control of production processes characterized by the possibility of performing operations (relevant to the processing of a set of jobs) with variable execution times. A production process relevant to a single machine is addressed first. An optimization problem with a quite general cost function is considered, and some properties of optimal solutions are derived. Then, a particular version of the problem is analyzed, in which the cost function is the weighted sum of the quadratic earliness and tardiness of jobs and of quadratic deviations between pre-defined nominal unitary processing times and the actual ones. The decision variables of the problem are the possible idle times inserted before job executions and the processing times of jobs. This single machine problem is stated as an optimal control problem and a closed-loop solution is derived. Then, a second production process is considered, in which multiple machines serve jobs in parallel, again with variable processing times and with different processing costs. With reference to this second production scheme, a significant decision problem refers to the splitting of jobs over the different machines. Then, on the basis of a sensitivity analysis of the single machine problem solution, some conditions to verify the optimality of a pre-defined splitting are derived. An on-line splitting scheme using such conditions is finally presented.

A Novel Pose Estimation Algorithm Based on Points to Regions Correspondence

A novel pose estimation algorithm is put forward in this paper. Given the points on an object and the convex regions in which the correspondent image points lie, the concrete values of position and orientation (t and R) between the object and the camera are found based on a points to regions correspondence. The unit quaternion representation of rotation matrix and convex Linear Matrix Inequalities (LMI) optimization methods are used to estimate the pose. By loosening the requirement of precise point to point correspondence and using convex LMI formulations, this algorithm provides a more robust and faster pose estimation method. The effect of this method is verified by simulation and laboratory experiment results.

An Algorithm for Image Denoising with Automatic Noise Estimate

In this paper, we propose a general algorithm for image denoising when no a priori information on the noise is available. The image denoising problem is formulated as an inequality constrained minimization problem where the objective is a general convex regularization functional and the right-hand side of the constraint depends on the noise norm and is not known. The proposed method is an iterative procedure which, at each iteration, automatically computes both an approximation of the noise norm and an approximate solution of the minimization problem. Experimental results demonstrate the effectiveness of the proposed automatic denoising procedure.

Optimal Control of Discrete Event Systems with Weakly Hard Real-Time Constraints

We consider Discrete Event Systems that can dynamically allocate resources in order to process tasks with real-time constraints. In the case of “weakly hard” constraints, a fraction of tasks is allowed to violate them, as long as m out of any k consecutive tasks meet their respective constraints. This is a generalization of a system with purely hard real-time constraints where m = k = 1. For non-preemptive and aperiodic tasks, we formulate an optimization problem where task processing times are controlled so as to minimize a cost function while guaranteeing that a “weakly hard” criterion is satisfied. We establish a number of structural properties of the solution to this problem which lead to an efficient algorithm that does not require any explicit nonlinear programming problem solver. The low complexity of this algorithm makes it suitable for on-line applications. Simulation examples illustrate the performance improvements in such optimally controlled systems compared to ad hoc schemes.

Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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Minimizing Place Capacities of Weighted Event Graphs for Enforcing Liveness

This paper addresses the problem of minimizing place capacities of weighted event graphs in order to enforce liveness. Necessary and sufficient conditions of the solution existence are derived. Lower bounds of place capacities while preserving liveness are established and a polynomial algorithm is proposed to determine an initial marking leading to these lower bounds while preserving the liveness.

Connecting the family with awareness systems

Awareness systems have attracted significant research interest for their potential to support interpersonal relationships. Investigations of awareness systems for the domestic environment have suggested that such systems can help individuals stay in touch with dear friends or family and provide affective benefits to their users. Our research provides empirical evidence to refine and substantiate such suggestions. We report our experience with designing and evaluating the ASTRA awareness system, for connecting households and mobile family members. We introduce the concept of connectedness and its measurement through the Affective Benefits and Costs of communication questionnaire (ABC-Q). We inform results that testify the benefits of sharing experiences at the moment they happen without interrupting potential receivers. Finally, we document the role that lightweight, picture-based communication can play in the range of communication media available.

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Optimal Control of Two-Stage Discrete Event Systems with Real-Time Constraints

We consider discrete event systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. When tasks are processed over a single stage, it has been shown that there are structural properties of the optimal sample path that lead to very efficient solutions of such problems. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, these properties no longer hold and no obvious extensions are known. We consider a two-stage problem with homogeneous cost functions over all tasks at each stage and derive several new optimality properties. These properties lead to the idea of introducing “virtual” deadlines at the first stage, thus partially decoupling the stages so that the known efficient solutions for single-stage problems can be used. We prove that the solution obtained by an iterative virtual deadline algorithm (VDA) converges to the global optimal solution of the two-stage problem and illustrate the efficiency of the VDA through numerical examples.

Architecture, implementation and application of complex event processing in enterprise information systems based on RFID

Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into service-oriented architecture, and the adoption of Radio Frequency Identification (RFID), event processing can fit well in enterprise information systems in terms of facilitation of event aggregation into high level actionable information, and event response to improve the responsiveness. To make it more applicable, the architecture of event processing in enterprise information systems is proposed; event meta model is put forth, and the rules are defined. To improve the detect efficiency, classification and partition of event instance is utilized. We have implemented the event processing mechanism in enterprise information systems based on RFID, including the data structures, optimization strategies and algorithm that is considered as one of the contributions. The performance evaluations show that the method is effective in terms of scalability and the capability of event processing. Complex event processing can improve operation efficiency and discover more actionable information, which is justified by the application.

OpenMP based parallel normalized direct methods for sparse finite element linear systems

A new parallel normalized exact inverse algorithm is presented for solving sparse symmetric finite element linear systems on symmetric multiprocessor systems (SMP), based upon an antidiagonal motion approach (“wave”-like pattern) for overcoming the data dependencies. The proposed algorithm was implemented using OpenMP directives. Numerical results, such as speedups and efficiency, are presented illustrating the efficient performance on a symmetric multiprocessor computer system, where the proposed algorithmic solution method achieves good speedups.

Triangulation of Points,Lines and Conics

The problem of reconstructing 3D scene features from multiple views with known camera motion and given image correspondences is considered. This is a classical and one of the most basic geometric problems in computer vision and photogrammetry. Yet, previous methods fail to guarantee optimal reconstructions—they are either plagued by local minima or rely on a non-optimal cost-function. A common framework for the triangulation problem of points, lines and conics is presented. We define what is meant by an optimal triangulation based on statistical principles and then derive an algorithm for computing the globally optimal solution. The method for achieving the global minimum is based on convex and concave relaxations for both fractionals and monomials. The performance of the method is evaluated on real image data.

High performance power control and opportunistic fair scheduling in TH-PPM UWB ad-hoc multimedia networks

Ultra wideband (UWB) systems are currently an important wireless infrastructure for high performance short-range communications and mobile applications. Indeed, forming ad-hoc networks among various UWB enabled devices is considered as an important mobile data exchange operating environment. In our study, we explore the problem of jointly optimizing the power level and data rate used in the devices in such a UWB based ad-hoc network. We propose a practical optimization algorithm based on judicious power control for real-time applications and opportunistic scheduling for non-real-time applications. Simulation results indicate that our proposed techniques are effective under various practical scenarios.

Protecting business intelligence and customer privacy while outsourcing data mining tasks

Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the storage requirement. This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.

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Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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Fast Blind Measurement of Blocking Artifacts in both Pixel and DCT Domains

The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video compression. In this paper, we propose a novel measurement algorithm for blocking artifacts. Computer simulation results indicate that the proposed method accurately measures the blocking artifacts without using the original image. Moreover, the proposed algorithm can be easily implemented in both pixel and DCT domains.

Interacting with mobile services: an evaluation of camera-phones and visual tags

We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive, although they had several important reservations about camera-phone technology more generally. Data from our pointing-device task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings, design lessons for camera-phone and visual-tag applications are presented.

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The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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A dominant predecessor duplication scheduling algorithm for heterogeneous systems

This paper proposes a new duplication-based task scheduling algorithm for distributed heterogeneous computing (DHC) systems. For such systems, many researchers have focused on solving the NP-complete problem of scheduling directed acyclic task graphs to minimize the makespan. However, the heterogeneity of computational resources and communication mechanisms poses some major obstacles to achieving high parallel efficiency. This paper proposes a heuristic strategy called the Dominant Predecessor Duplication (DPD) scheduling algorithm, which allows for system heterogeneities and communication bandwidth to exploit the potential of parallel processing. This algorithm can improve system utilization and avoid redundant resource consumption, resulting in better schedules. Experimental results show that the system heterogeneities and program structures of applications affect scheduling performance, and that our presented algorithm is better able to avoid these problems than those presented in previous literature. Here, we show that our algorithm can be applied to design efficient distributed systems to overcome performance bottlenecks caused by system heterogeneities.