Search

Within substations installed with flexible ac transmission systems, the magnetic flux leakage (MFL) generated by air core reactors in static var compensator (SVC) can be very high. To reduce the influence of the high magnetic field, an optimal suppression method is selected based on fuzzy comprehensive evaluation. First, a series of onsite measurements are performed around the reactors in the SVC. From the measurement results, the low-voltage cables, dc system, grounding grid, and magnetic flux density around the air core reactors are seriously affected by the MFL generated by the air core reactors with currents of a few kiloamperes. Then, according to the actual parameters of the air core reactors, a three-dimensional simulation model is constructed using the finite-element method (FEM) to calculate the magnetic flux density and some parameters related to the magnetic field. Moreover, four different suppression methods are proposed to suppress the MFL influence. Due to the multiple aspects affected by MFL, it is always difficult to make a quantitative comparison for the suppression methods based on previous research. For this reason, a fuzzy comprehensive evaluation method based on a linear evaluation model is proposed to quantitatively compare the suppression methods by using the suppression effect value δ. In a comparison of different suppression methods, the aluminum plate shielding with proper parameters has prove to be an optimal method for achieving a comprehensive suppression effect. The modeling and evaluation methods presented in this article are both valuable to solve the complex MFL influence problem in different SVC-based substations. [ABSTRACT FROM AUTHOR]

Despite many existing research on data forwarding in low-duty-cycle wireless sensor networks (WSNs), relatively little work has been done on energy-efficient data forwarding with probabilistic delay bounds. Probabilistic delay guarantees (i.e., delay bounded data delivery with reliability constraints) are of increasing importance for many delay-constrained applications, since deterministic delay bounds are prohibitively expensive to guarantee in WSNs. However, radio duty-cycling and unreliable wireless links pose challenges for achieving the probabilistic delay guarantee in WSNs. In this paper, we propose EEAF, a novel energy-efficient adaptive forwarding technique tailored for low-duty-cycle WSNs with unreliable wireless links. We show the existence of path diversity in low-duty-cycle WSNs, where delay-optimal routing and energy-optimal routing are likely following different paths. The key idea of EEAF is to exploit the intrinsic path diversity to provide probabilistic delay guarantees while minimizing transmission cost. In EEAF, an early arriving packet will be adaptively switched to the energy-optimal path for energy conservation. Delay quantiles are derived at each node in a distributed manner and are used as the guidelines in the adaptive forwarding decision making. Extensive testbed experiment and large-scale simulation show that EEAF effectively reduces the transmission cost by 12%~25% with probabilistic delay guarantees under various network settings. In addition, we extend the EEAF technique with data aggregation for event-based traffic scenarios. Evaluation using publicly available WSN event traffic traces yields very encouraging results with up to 40% energy saving in probabilistic delay bounded data delivery. [ABSTRACT FROM AUTHOR]

Surgery scheduling is of critical importance, because an operating room (OR) is the major cost generating unit in the hospital. However, schedulers face tremendous challenges brought by case cancellation, which have been observed in most departments. On the other hand, the randomness of surgery duration also has a significant impact on an OR schedule. In this paper, we develop a stochastic integer programming model for multiple ORs that simultaneously considers the uncertainties of case cancellation and surgery duration. We aim at minimizing the costs from the perspectives of both health care providers and patients. The Benders decomposition is used to address the computational complexity. A series of experiments is conducted to show the effectiveness of the proposed model and solution approaches. A case study based on two departments at West China Hospital is carried out, where the total cost can be reduced by approximately 27%. A sensitivity analysis is conducted in the case study, from which we gain managerial insights. Note to Practitioners—The effectiveness and efficiency of operating room (OR) scheduling are highly valued by hospital practitioners, as the OR department is one of the most resource-intensive units in a hospital. The uncertainty of surgery duration and patient cancellation in OR scheduling brings a tremendous challenges to the decision makers. We propose a stochastic integer programming model by taking these uncertain factors into consideration. The proposed model significantly outperforms the current practice according to our case study. We also conduct a sensitivity analysis to obtain managerial insights. The value of stochastic solutions is calculated to show the importance of considering both the uncertain factors. [ABSTRACT FROM AUTHOR]