Optimal multisensor data fusion for linear systems with missing measurements

Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.

Optimal linear data fusion for systems with missing measurements

In this paper, we provide the optimal data fusion filter for linear systems suffering from possible missing measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. The data fusion process is made on the raw data provided by two sensors  observing the same entity. Each of the sensors is losing the measurements in its own data loss rate. The data fusion filter is provided in a recursive form for ease of implementation in real-world applications.

Optimal actuator fault tolerance for static nonlinear systems based on minimum output velocity jump

Fault tolerance for a class of non linear systems is addressed based on the velocity of their output variables. This paper presents a mapping to minimize the possible jump of the velocity of the output, due to the actuator failure. The failure of the actuator is assumed as actuator lock. The mapping is derived and it provides the proper input commands for the healthy actuators of the system to tolerate the effect of the faulty actuator on the output of the system. The introduced mapping works as an optimal input reconfiguration for fault recovery, which provides a minimum velocity jump suitable for static nonlinear systems. The proposed mapping is validated through different case studies and a complementary simulation. In the case studies and the simulation, the mapping provides the commands to compensate the effect of different faults within the joints of a robotic manipulator. The new commands and the compare between the velocity of the output variables for the health and faulty system are presented.

Sizing solar home systems for optimal development impact

The paper compares the development impact of three different sized solar home systems (SHS) (10, 40 and 80 Wp) installed in rural East Timor. It describes research aimed to determine whether the higher cost of the larger systems was justified by additional household benefits. To assess the development impact of these different sizes of SHS the research used a combination of participatory and quantitative tools. Participatory exercises were conducted with seventy-seven small groups of SHS users in twenty-four rural communities and supplemented with a household survey of 195 SHS users.

The combined results of these evaluation processes enabled the three sizes of SHS to be compared for two types of benefits—those associated with carrying out important household tasks and attributes of SHS which were advantageous compared to the use of non-electric lighting sources. The research findings showed that the small, 10 Wp SHS provided much of the development impact of the larger systems. It suggests three significant implications for the design of SHS programs in contexts such as East Timor: provide more small systems rather than fewer large ones; provide lighting in the kitchen wherever possible; and carefully match SHS operating costs to the incomes of rural users.

Optimal design of traffic signal controller using neural networks and fuzzy logic systems

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Neural network (NN) and fuzzy logic system (FLS) are two methods applied to develop intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The training approach and data for both these learning methods are similar. Both methods use genetic algorithm to tune their parameters during learning. Finally, The performance of the two intelligent learning methods is compared with the performance of simple fixed-time method. Simulation results indicate that both intelligent methods significantly reduce the total delay in the network compared to the fixed-time method.

Improving the performance of equalization in communication systems

In this paper, research on exploring the potential of several popular equalization techniques while overcoming their disadvantages has been conducted. First, extensive literature survey on equalization is conducted. The focus has been placed on several popular linear equalization algorithm such as the conventional least-mean-square (LMS) algorithm, the recursive least squares (RLS) algorithm, the fi1tered-X LMS algorithm and their development. The approach in analysing the performance of the filtered-X LMS Algorithm, a heuristic method based on linear time-invariant operator theory is provided to analyse the robust perfonnance of the filtered-X structure. It indicates that the extra filter could enhance the stability margin of the corresponding non filtered X structure. To overcome the slow convergence problem while keeping the simplicity of the LMS based algorithms, an H2 optimal initialization is proposed.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Performance optimization for energy-aware adaptive checkpointing in embedded real-time systems

Using additional store-checkpoinsts (SCPs) and compare-checkpoints (CCPs), we present an adaptive checkpointing for double modular redundancy (DMR) in this paper. The proposed approach can dynamically adjust the checkpoint intervals. We also design methods to calculate the optimal numbers of checkpoints, which can minimize the average execution time of tasks. Further, the adaptive checkpointing is combined with the DVS (dynamic voltage scaling) scheme to achieve energy reduction. Simulation results show that, compared with the previous methods, the proposed approach significantly increases the likelihood of timely task completion and reduces energy consumption in the presence of faults.

Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations

The use of Kalman filtering is very common in state estimation problems. The problem with Kalman filters is that they require full prior knowledge about the system modeling. It is also assumed that all the observations are fully received. In real applications, the previous assumptions are not true all the time. It is hard to obtain the exact system model and the observations may be lost due to communication problems. In this paper, we consider the design of a robust Kalman filter for systems subject to uncertainties in the state and white noise covariances. The systems under consideration suffer from random interruptions in the measurements process. An upper bound for the estimation error covariance is proposed. The proposed upper bound is further minimized by selection of optimal filter parameters. Simulation example shows the effectiveness of the proposed filter.

Simulation-based input loading condition optimisation of airport baggage handling systems

Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.