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Abstract In geostatistical simulation, a realization represents one possible outcome of the spatial uncertainty model. Tens to hundreds of realizations are generated in order to understand response property variation. There are ways to summarize local uncertainty, but visualizing all realizations is important to understand joint uncertainty between multiple locations. There is no straightforward manner to visualize all realizations at the same time or in sequence. This paper presents a new method to sequentially display multiple geostatistical realizations. The proposed algorithm performs an ordering of the visible portion of the realizations (images), according to the distance between realizations. The concept of distance corresponds to the differences computed cell by cell for every realization pair or to the differences computed from a moving window filtering applied to each realization. To define an optimal sequence of realizations, the shortest path route through the realizations is established by a simulated annealing technique. The gradual transition between realizations is enhanced by an image morphing technique where intermediate images are introduced between the original images. The final result consists of an animation that shows the sequence of realizations and allows better understanding of the uncertainty model.

The performance of wireless multihop networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals and therefore allowing multiple concurrent transmissions. Effectively managing the transmission concurrency over the shared medium ensures good quality of transmission and therefore results in higher achievable transmission data rates. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multi-rate multihop wireless network. To characterize the network performance under these characteristics, we follow a cross-layer design approach and formulate the joint routing and scheduling problem with rate control as a mixed integer linear program with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. However, the complexity of solving exactly the pricing subproblem limits the application of the model to very small size network instances. We develop one efficient greedy method for solving exactly the pricing subproblem as well as a simulated annealing based heuristic approach with very good performance. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates result in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reap the largest benefits with gains exceeding 20% over networks only with SIC capabilities or only with rate control.

Abstract This paper presents a method for quantifying drivers’ driving skills using closed-loop dynamic simulations of articulated heavy vehicles (AHVs). Due to AHVs’ multi-unit configurations, large sizes, and high centers of gravity (CGs), these large vehicles exhibit poor directional performance. AHVs require wide roads and large radii of path curvature for evasive maneuvers; these large vehicles frequently display unstable motion modes, including trailer sway, jackknifing, and rollover. The directional performance of AHVs is frequently evaluated in terms of maneuverability and lateral stability. There exists a trade-off between the performance measures of maneuverability and lateral stability. An AHV, its driver and the road constitute a unique closed-loop dynamic system. The unique dynamic characteristics of AHVs impose significant challenges for safe operations of these vehicles. However, little attention has been paid to the interactions of driver-AHV. This paper tackles the problem of quantifying drivers’ driving skills considering the interactions of driver-AHV under simulated single lane-change (SLC) maneuvers. Based on two driver characteristic parameters, namely preview time (PT) and transport delay (TD), we propose two performance measures, i.e., path-following score (PFS) and combined stability score (CSS), as the indicators for assessing the driving skills of AHV drivers under the simulated maneuvers. The driver-AHV closed-loop dynamic simulation is implemented using the built-in driver model and virtual vehicles developed in TruckSim. The numerical simulation results for rearward amplification (RA) measures are validated using driver-in-the-loop (DIL) real-time simulation. The simulation identifies AHV drivers’ driving skills in terms of lateral stability, path-following, as well as balanced path-following and stability regions considering PT and TD properties.

In this work, significant enhancement in absorption, especially in the near infra-red region, is achieved for ultra-thin crystalline silicon of various thicknesses using a photonic crystal structure. This is realized by patterning the silicon with close-packed inverted pyramid structuring, wherein the lattice spacing is comparable to the wavelength of the near infra-red light, which results in strong wave-interference-based light trapping. Absorption enhancement of more than a factor of two in the spectral range of 1000–1100 nm is achieved without any other optical enhancments. Details on the techniques used to realize the ultra-thin silicon, the fabrication of the inverted pyramids using standard photolithography and a complete optical analysis are reported in this work.

This paper develops enhanced hybrid generic (nonlinear) models of Type-3 and Type-4 wind power plants (WPPs) and extracts the corresponding linear (small-signal) dynamic models for power system transient stability analysis. The models are hybrid in nature since they consider both continuous states and discrete logic-controlled variables. The introduced enhancements include (i) a freezing function to reactivate reactive power emulator of Type-3 and Type-4 WPPs, (ii) active-current command recalculation step for Type-3 WPP and (iii) elimination of an activating logic of PI-controller limits in real current control path. The main feature of the enhanced models is that they can replicate the field-verified responses of the built-in PSS/E software models in any adopted software platform. It should be noted that the generic models described in the technical literature do not necessarily provide such replication. The paper also deduces small-signal dynamic models of Type-3 and Type-4 WPPs and addresses the multiple eigen structures of the linearized enhanced generic model of Type-3 WPP, which has not been comprehensively discussed in the technical literature. The enhanced nonlinear hybrid models and the corresponding linearized models are evaluated and verified based on time-domain simulation studies in PSS/E and MATLAB platforms, using NPCC system as the test bed.

Abstract Fuzzy arithmetic operations are applied to mathematical equations that include fuzzy numbers, which are commonly used to represent non-probabilistic uncertainty in different applications. Although there are two mathematical approaches available in the literature for implementing fuzzy arithmetic (i.e., the α-cut approach, and the extension principle approach), the existing computational methods are mainly focused on implementing the α-cut approach due to its simplicity. However, this approach causes overestimation of uncertainty in the resulting fuzzy numbers, a phenomenon that reduces the interpretability of the results. This overestimation can be reduced by implementing fuzzy arithmetic using the extension principle; however, existing computational methods for implementing the extension principle approach are limited to the use of min and drastic product t-norms. Using the min t-norm produces the same result as the α-cuts and interval calculations approach, and the drastic product t-norm is criticized for producing resulting fuzzy numbers that are highly sensitive to the changes in the input fuzzy numbers. This paper presents original computational methods for implementing fuzzy arithmetic operations on triangular fuzzy numbers using the extension principle approach with product and Lukasiewicz t-norms. These computational methods contribute to the different applications of fuzzy arithmetic; they reduce the overestimation of uncertainty, as compared to the α-cut approach, and they reduce the sensitivity of the resulting fuzzy numbers to changes in the input fuzzy numbers, as compared to the extension principle approach using drastic product t-norm.

Known as multihoming, devices with more than one network interface can enhance their performance capabilities by harnessing unused resources from alternative access networks. Whether it's improved reliability or sheer throughput potential, network devices will benefit from a multihomed framework. Unfortunately, our current means of guaranteeing reliability while maintaining quality control, specifically, the transmission control protocol (TCP), does not support multihoming. Despite the latter, a relatively young transport layer standard called the stream control transmission protocol (SCTP), incorporates multihoming into its design. In this paper, we investigate state-of-the-art multihoming techniques using SCTP. A comprehensive survey of developments has brought forth three main research areas, namely: handover management, concurrent multipath transfer (CMT), and cross-layer activities. While the presented algorithms may offer sufficient results, many open problems still remain.

Abstract The swing temperature usually leads to the parameter drift and even failure in Electric power steering (EPS), which is a potential threat for safe driving of automobiles. Power Metal-Oxide-Semiconductor Field Effect Transistors (MOSFET) are a primary heat source in EPS and, therefore, can directly represent the temperature fluctuation of EPS. Existing technologies that observe the temperatures of MOSFETs are restricted by operating conditions, cost and implementation difficulty for application in automotive EPS systems. To address this issue, a novel real-time junction temperature observation method is proposed by combining a Foster network with an Extended State Observer (ESO) technique. Transfer paths of thermal resistance in EPS converters are analyzed, and the thermal state space model is established by the Foster network. The temperature ESO of the EPS controller is then developed based on this thermal model to estimate the temperature and perturbation. Confirmation tests that introduce a simulated driving steering method measure the MOSFET temperature and verify the validity of the temperature ESO. The test results demonstrate that the proposed temperature ESO can be used to monitor junction temperatures of MOSFETs in EPS.

Internet traffic has been shown to have long-range dependence, and is often modeled by using the fractional Gaussian noise model. The fractional Gaussian noise model can capture the autocorrelation of a real trace, but cannot fit the marginal distribution when the trace has a non-Gaussian marginal distribution. In this letter, we use the inverted Box-Cox transformation to establish a long-range dependent Internet traffic model that can simultaneously capture both the long-range dependence parameter and the marginal distribution of a real trace