Multilevel rationing policy for spare parts when demand is state dependent

OR Spectrum - The multilevel rationing (MR) policy is the optimal inventory control policy for single-item M / M / 1 make-to-stock queues serving different...     

A new switched-capacitor/switched-inductor–based converter with high voltage gain and low voltage stress on switches

In this paper, a nonisolated transformerless switched-capacitor/switched-inductor–based dc-dc converter with high voltage gain is proposed. The proposed converter has low voltage stress on switches and diodes which leads to decrease the switching losses. The voltage gain of the proposed converter can be increased by adding more diode-capacitor modules at output side; therefore, the proposed converter has expandable structure. The proposed converter has higher voltage gain and lower voltage stress on switches comparing with the other similar switched-capacitor–based converters. The proposed converter uses two switches with the same switching pattern. Therefore, its switching pattern is not complicated. In this paper, the proposed converter is analyzed during a switching period which has two operating modes. Moreover, the average current through the switches, diodes, and inductors; voltage stress on switches and diodes; voltage gain; maximum and minimum current of switches and diodes; and the efficiency curve are calculated. Finally, in order to verify the accuracy performance of the proposed converter, a 530-W 40 to 920-V prototype is implemented practically.     

Expandable interleaved high voltage gain boost DC-DC converter with low switching stress

In this paper, an interleaved DC-DC step-up boost converter with high voltage conversion ratio and low voltage stresses on switches and diodes is proposed. The proposed converter has low average current passing through the diodes and switches and low input current ripple as a feature of interleaved converters. The voltage gain of the proposed converter can be increased by adding more diode-capacitor modules; therefore, the proposed converter has expandable structure. In addition, by implementing more diode-capacitor modules, the switching stresses would be more decreased. Also, to evaluate the performance of the proposed converter, it is compared with other similar presented circuits in the literature. The proposed converter is not only able to provide higher voltage gain but also has lower voltage stresses on switches and diodes. Consequently, switches and diodes with low voltage ratings can be selected. Theoretical analysis is provided in this study for each operation mode and the average current through the switches, diodes and inductors, voltage stresses on switches and diodes, voltage gain, and input current ripple are calculated. Finally, to demonstrate the accuracy performance of the proposed converter, a 450-W prototype is implemented practically.     

A new topology for nonisolated multiport zero voltage switching dc‐dc converter

In this paper, a new multiport zero voltage switching dc‐dc converter is proposed. Multiport dc‐dc converters are widely applicable in hybrid energy generating systems to provide substantial power to sensitive loads. The proposed topology can operate in 3 operational modes of boost, buck, and buck‐boost. Moreover, it has zero voltage switching operation for all switches and has the ability to eliminate the input current ripple; also, at low voltage side, the input sources can be extended. In addition, it has the ability of interfacing 3 different voltages only by using 3 switches. In this paper, the proposed topology is analyzed theoretically for all operating modes; besides, the voltage and current equations of all components are calculated. Furthermore, the required soft switching and zero input currents ripple conditions are analyzed. Finally, to demonstrate the accurate performance of the proposed converter, the Power System Computer Aided Design(PSCAD)/Electro Magnetic Transient Design and Control(EMTDC) simulation and experimental results are extracted and presented.     

Upper and lower bounds for dynamic cluster assignment for multi-target tracking in heterogeneous WSNs

In this paper, we consider the problem of cluster task assignment to maximize total utilities of nodes for target coverage in heterogeneous Wireless Sensor Networks. We define this problem as assigning the tasks of Cluster Head (CH) and Cluster Members (CM) to nodes for each target and requiring communication connectivity between every CH with its members. The utility of each node for each target is defined as a function of its distance to the target and its remaining energy. We propose an upper bound based on Lagrangian Relaxation (LR) and a lower bound by Linear Programming (LP) relaxation with a combination of Randomized Rounding (RR) and a greedy-based heuristic. Furthermore, we propose a distributed heuristic algorithm based on matching and a general assignment problem. Dynamic movements of targets are taken into account by intra/inter-cluster task reassignments. Simulation results, compared with optimal values, reveal that the LR upper bound performs better than the bound reached by pure LP relaxation. The lower bound obtained by LP relaxation combined with the RR technique provides close results in comparison with the distributed heuristic algorithm. Furthermore, the results of the distributed heuristic algorithm remain between the upper and lower bounds and close to optimal values.     

Primal and dual-based algorithms for sensing range adjustment in WSNs

Coverage and tracking of multiple targets, are viewed as important challenges in WSNs, mainly aimed for future ubiquitous and pervasive applications. Target coverage in WSNs with large numbers of sensor nodes and targets, and with a predefined placement of sensors, may be conducted through adjusting the sensing range and considering the energy consumption related to this operation. In this paper, we encounter the problem of multiple target coverage in WSNs by determining the sensing range of each sensor node to maximize the total utility of the network. We solve this Network Utility Maximization (NUM) problem via two approaches, primal and dual decompositions, which result in iterative distributed price-based algorithms. Convergence of sensing ranges to optimal values is proved by means of stability analysis and simulation experiments. Simulation results show convergence to optimal values in few iterations, with near optimal values for the total objective function and energy consumption of nodes. These results show scalability of our algorithm, in terms of the number of iterations needed for convergence when compared with the other two methods. Furthermore, the distributed algorithm based on dual decomposition is used to cover efficiently moving targets in consecutive time intervals.     

Entanglement degradation in the presence of the Kerr–Newman black hole

We investigate bipartite quantum correlations in the presence of the four-dimensional Kerr–Newman black hole using the negativity as a measure for the entanglement. We assume Alice and Rob initially share a maximally entangled state, and then Rob accelerates toward the event horizon \(h_{+}\). We find that when Rob accelerates uniformly toward the external horizon, the entanglement degrades for the Alice–Rob system and this degradation increases as Rob gets closer to the horizon. It is found that for the case Alice–AntiRob, no creation of quantum correlation occurs. Finally, we investigate the bipartite entanglement using an alternative entanglement measure, namely generalized concurrence, and we show that the results are in consistent with those obtained by negativity.     

Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

Prediction in a small-sized sample with a large number of covariates, the “small n, large p” problem, is challenging. This setting is encountered in multiple applications, such as in precision medicine, where obtaining additional data can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the relevance of the covariates, or of values of the regression coefficients, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of the proposed method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.     

Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks

This article presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss–Markov process. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as a difference-of-convex program and apply the convex-concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and a model for 2D target tracking by a drone swarm for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity-promoting, and an effective sensor selection heuristic.     

KINETICS OF ADSORPTION OF ORGANICS FROM AN ABOVE GROUND OIL SHALE RETORT WATER

Activated carbon was used to remove organics from retort waters produced in an above ground oil shale retort. This work included batch equilibrium experiments and continuous packed bed studies. The equilibrium isotherms were nonlinear over the entire liquid concentration range (0 to 2880 mg/L). Break through curves were obtained in packed bed experiments as a function of bed length, particle size, and liquid velocity. Rates of adsorption and mass transfer coefficients were evaluated from the breakthrough curves.