A new method to test system diagnosability

A new necessary and sufficient condition for t-diagnosability of a system is proposed. It is shown that the new scheme is computationally more efficient than the existing one for checking the diagnosability of a system.     

The Effects of Learning on Optimal Lot Size Determination—Multiple Product Case

This article extends the analysis of an earlier paper by the authors for a single product to lot size determination for manufacturing cycles of multiple products. The effects of production breaks, learning interactions due to commonality of tasks and different learning rates for products are considered in the formulation of an equation for the average production time per unit. The equation is applied to an equal lot size per product model. An analysis of a model restricted to equal production intervals per product per cycle is also presented. A sample problem is solved to demonstrate the application of the effects of learning to multiple product lot size determination.     

On the thermodynamics of oxygen in molten copper,Cu-Sn,and Cu-Ag alloys

Measurements of oxygen activity in molten copper, Cu-Sn, and Cu-Ag alloys at 1135°C have been made utilizing the solid-state electrolyte technique. The activity coefficient of oxygen in molten copper at low oxygen levels (<0.1 pet) was found to be 0.12. The interaction parameters of silver and tin on oxygen were found to be ε _O ^Ag = 4.52 and ε _O ^Sn =-10.5. Comparison of experimental data with solution models revealed that the behavior of oxygen in Cu-O-Ag alloys is in accordance with predictions of Alcock and Richardson’s quasichemical model when a coordination number between 1 and 4 is assumed, depending on the silver and oxygen content. Furthermore, agreement with Belton’s quasichemical model based on the assumption of Ag-O dipoles serves to strengthen the evidence for the existence of Ag-O species in solution. The behavior of oxygen in the Cu-O-Sn system also shows better agreement with the quasichemical model than with the random model, with low coordination numbers favored in dilute tin solutions, increasing to larger values in Cu-60 Sn solutions.     

Network Assisted Power Control for Wireless Data

The cellular telephone success story prompts the wireless communications community to turn its attention to other information services, many of them in the category of wireless data communications. One lesson of cellular telephone network operation is that effective power control is essential to promote system quality and efficiency. In recent we have applied microeconomic theories to power control taking into account notions of utility and pricing. Our earlier work has shown that this new approach to power control for wireless data performs better than traditional techniques applied for voice signals. However, the operating points of such a strategy result in an unfair equilibrium in that users operate with unequal signal-to-interference ratios. Further, the power control algorithms required to achieve such operating points are more complex than the simple signal-to-interference ratio balancing algorithms for voice. In this paper, we introduce a new concept, Network Assisted Power Control (NAPC) that maximizes utilities for users while maintaining equal signal-to-interference ratios for all users. The power control algorithm is easily implemented via signal-to-interference ratio balancing with the assistance of the network that broadcasts the common signal-to-interference ratio target.     

Electroactive Shape Memory Effect of Polyurethane Composites Filled with Carbon Nanotubes and Conducting Polymer

We report the electroactive shape memory composites obtained by shape memory polyurethane block copolymer (PU) and multi-walled carbon nanotubes (MWNTs), and polypyrrole (PPy). An addition of combined MWNTs and PPy contributed to an enhancement in conductivity of PU-MWNTs composites. PU containing 2.5% MWNTs showed better mechanical and thermal properties than other composites, but conductivity was not sufficient for showing the shape memory effect by applying electrical voltages. However, when the composite was lightly coated by PPy (2.5%), its conductivity was the highest than other composites. Such the conductivity of this composite was enough to show electroactive shape recovery by heating above transition temperature of 40-48°C due to melting of polycaprolactone soft segment domain. The good shape recovery of 90-96% could be obtained in the shape recovery test when an electric field of 25 V was applied.     

Change detection for moving object segmentation with robust background construction under Wronskian framework

Although background subtraction techniques have been used for several years in vision systems for moving object detection, many of them fail to provide good results in presence of noise, illumination variation, non-static background, etc. A basic requirement of background subtraction scheme is the construction of a stable background model and then comparing each incoming image frame with it so as to detect moving objects. The novelty of the proposed scheme is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes. The results obtained by the proposed model is found to provide accurate shape of moving objects. The effectiveness of the proposed scheme is verified by comparing the results with those of some of the existing state of the art background subtraction techniques on public benchmark databases. We found that the average F-measure is significantly improved by the proposed scheme from that of the state-of-the-art techniques.     

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.     

Stochastic contraction-based observer and controller design algorithm with application to a flight vehicle

This paper focuses on a stochastic version of contraction theory to construct observer-controller structure for a flight dynamic system with noisy velocity measurement. A nonlinear stochastic observer is designed to estimate the pitch rate, the pitch angle, and the velocity of an aircraft example model using stochastic contraction theory. Estimated states are used to compute feedback control for solving a tracking problem. The structure and gain selection of the observer is carried out using Itô's stochastic differential equations and the contraction theory. The contraction property of integrated observer-controller structure is derived to ensure the exponential convergence of the trajectories of closed-loop nonlinear system. The upper bound of the state estimation error is explicitly derived and the efficacy of the proposed observer-controller structure has been shown through the numerical simulations.