Forest Carbon Sequestration under the U.S. Biofuel Energy Policies

This paper analyzes impacts of the U.S. biofuel energy policies on the carbon sequestration by forest products, which is expressed as Harvested Wood Products (HWP) Contribution under the United Nations Framework Convention on Climate Change. Estimation for HWP Contribution is based on tracking carbon stock stored in wood and paper products in use and in solid-waste disposal sites (SWDS) from domestic consumption, harvests, imports, and exports. For this analysis, we hypothesize four alternative scenarios using the existing and pending U.S. energy policies by requirements for the share of biofuel to total energy consumption, and solve partial equilibrium for the U.S. timber market by 2030 for each scenario. The U.S. Forest Products Module (USFPM), created by USDA Forest Service Lab, operating within the Global Forest Products Model (GFPM) is utilized for projecting productions, supplies, and trade quantities for the U.S. timber market equilibrium. Based on those timber market components, we estimate scenario-specific HWP Contributions under the Production, the Stock Change, and the Atmospheric Approach suggested by Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories using WOODCARB II created by VTT Technical Research Centre of Finland and modified by USDA Forest Service Lab. Lastly, we compare estimated results across alternative scenarios. Results show that HWP Contributions for the baseline scenario in 2009 for all approaches are estimated higher than estimates reported by U.S. Environmental Protection Agency in 2011, (e.g., 22.64 Tg C/ year vs 14.80 Tg C/ year under the Production Approach), which is due to the economic recovery, especially in housing construction, assumed in USFPM/GFPM. Projected HWP Contribution estimates show that the Stock Change Approach, which used to provide the highest estimates before 2009, estimate HWP Contribution lowest after 2009 due to the declining annual net imports. Though fuel wood consumption is projected to be expanded as an alternative scenario requires higher wood fuel share to total energy consumption, the overall impacts on the expansion in other timber products are very modest across scenarios in USFPM/GFPM. Those negligible impacts lead to small differences of HWP Contribution estimates under all approaches across alternative scenarios. This is explained by the points that increasing logging residues are more crucial for expansion in fuel wood projections rather than the expansion of forest sector itself, and that the current HWP Contribution does not include carbon held in fuel wood products by its definition.Forest Products, Carbon Sequestration, Biofuel Policies, HWP Contribution, Resource /Energy Economics and Policy,

Theory and Application of the Chapman Heartbar Horseshoe for Laminitis

In the past fifteen years great advances have been made in understanding the pathogenesis and treatment of laminitis. One of these advances is the use of the heartbar shoe. In theory the Chapman heart bar should work well for the treatment of laminitis, however, in practice favorable results depend on the proper application of the shoe along with supportive care of the foot

Inertial sensor array processing with motion models

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordBy arranging a large number of inertial sensors in an array and fusing their measurements, it is possible to create inertial sensor assemblies with a high performance-to-price ratio. Recently, a maximum likelihood estimator for fusing inertial array measurements collected at a given sampling instance was developed. In this paper, the maximum likelihood estimator is extended by introducing a motion model and deriving a maximum a posteriori estimator that jointly estimates the array dynamics at multiple sampling instances. Simulation examples are used to demonstrate that the proposed sensor fusion method have the potential to yield significant improvements in estimation accuracy. Further, by including the motion model, we resolve the sign ambiguity of gyro-free implementations, and thereby open up for implementations based on accelerometer-only arrays

Smartphone-based vehicle telematics: a ten-year anniversary

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordJust as it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphone-based automotive navigation, and survey the state of the art in smartphone-based transportation mode classification, vehicular ad hoc networks, cloud computing, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment

Alternative EM algorithms for nonlinear state-space models

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordThe expectation-maximization algorithm is a commonly employed tool for system identification. However, for a large set of state-space models, the maximization step cannot be solved analytically. In these situations, a natural remedy is to make use of the expectation-maximization gradient algorithm, i.e., to replace the maximization step by a single iteration of Newton’s method. We propose alternative expectationmaximization algorithms that replace the maximization step with a single iteration of some other well-known optimization method. These algorithms parallel the expectation-maximization gradient algorithm while relaxing the assumption of a concave objective function. The benefit of the proposed expectation-maximization algorithms is demonstrated with examples based on standard observation models in tracking and localization

IMU-based smartphone-to-vehicle positioning

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn this paper, we address the problem of using inertial measurements to position a smartphone with respect to a vehicle-fixed accelerometer. Using rigid body kinematics, this is cast as a nonlinear filtering problem. Unlike previous publications, we consider the complete three-dimensional kinematics, and do not approximate the angular acceleration to be zero. The accuracy of an estimator based on the unscented Kalman filter is compared with the Cramer-Rao bound. As is illustrated, the estimates can be expected to be better in the horizontal plane than in the vertical direction of the vehicle frame. Moreover, implementation issues are discussed and the system model is motivated by observability arguments. The efficiency of the method is demonstrated in a field study which shows that the horizontal RMSE is in the order of 0.5 [m]. Last, the proposed estimator is benchmarked against the state-of-the-art in left/right classification. The framework can be expected to find use in both insurance telematics and distracted driving solutions

Fusion of OBD and GNSS Measurements of Speed

This is the author accepted manuscript. The final version is available from Institute of Electrical and Electronics Engineers (IEEE) via the DOI in this record.There are two primary sources of sensor measurements for driver behavior profiling within insurance telematics and fleet management. The first is the on-board diagnostics system, typically found within most modern cars. The second is the global navigation satellite system, whose associated receivers commonly are embedded into smartphones or off-the-shelf telematics devices. In this paper, we present maximum likelihood and maximum a posteriori estimators for the problem of fusing speed measurements from these two sources to jointly estimate a vehicle's speed and the scale factor of the wheel speed sensors. In addition, we analyze the performance of the estimators by use of the Cramér-Rao bound, and discuss the estimation of model parameters describing measurement errors and vehicle dynamics. Last, simulations and real-world data are used to show that the proposed estimators yield a substantial performance gain compared to when employing only one of the two measurement sources