A neural method for geographical continuous field estimation

Geographical continuous fields are usually represented by means of sample points or zonal attribute sets, which leads to discontinuities in attribute values, or unrealistic homogeneities. The solution is to interpolate and extrapolate from known values, according to some constraints upon the estimation. Then, a new estimation method is proposed, based on finite difference method results, and the Hopfield neural network. This new method meets our statistical and morphological constraints.     

Operational set theory and small large cardinals

A new axiomatic system OST of operational set theory is introduced in which the usual language of set theory is expanded to allow us to talk about (possibly partial) operations applicable both to sets and to operations. OST is equivalent in strength to admissible set theory, and a natural extension of OST is equivalent in strength to ZFC. The language of OST provides a framework in which to express “small” large cardinal notions—such as those of being an inaccessible cardinal, a Mahlo cardinal, and a weakly compact cardinal—in terms of operational closure conditions that specialize to the analogue notions on admissible sets. This illustrates a wider program whose aim is to provide a common framework for analogues of large cardinal notions that have appeared in admissible set theory, admissible recursion theory, constructive set theory, constructive type theory, explicit mathematics, and systems of recursive ordinal notations that have been used in proof theory.     

Evaluation of decision-tree rating scales for mental workload estimation

Recent studies suggest that a decision-tree rating scale called the Modified Cooper-Harper (MCH) rating scale is a globally sensitive indicator of change in mental loading (Wierwille and Casali 1983 a). The present study was directed at developing refinements in the scale and at obtaining additional background information. The MCH scale and five design variations of the scale were studied in two independent aircraft-simulator experiments. Aspects studied were the decision-tree structure, the number of categories, the decision sequence and the effects of computer implementation. Results using the rating scales indicate that the MCH scale and its computerized version are generally more consistent than the others. Attendant questionnaire results indicate that pilots base their ratings on the same factors that researchers believe are the important elements of the multidimensional construct of workload.     

Land cover change detection at coarse spatial scales based on iterative estimation and previous state information

This study focuses on the use of coarse spatial resolution (CR, pixel size about 1 km²) remote sensing data for land cover change detection and qualification. Assuming the linear mixing model for CR pixels, the problem is that both the multitemporal class features and the pixel composition in terms of classes are unknown. The proposed algorithm is then based on the iterative alternate estimation of each unknown variable. At each iteration, the class features are estimated, thanks to the knowledge of the composition of some pixels, and then the pixel composition is re-estimated knowing the class features. The subset of known composition pixels is the subset of pixels where no change has occurred, i.e. the previous land cover map is still valid. It is derived automatically by removing at each iteration the pixels where the new composition estimation disagrees with the former one. Finally, for the final estimation of the pixel composition, a Markovian chain model is used to guide the solution, i.e. the previous land cover map is used as a ‘reminder’ or ‘memory’ term.This approach has been first validated using simulated data with different spatial resolution ratios. Then, the detection of forest change with SPOT/VGT-S10 has been considered as an actual application case. Finally, the method has been applied to change detection on the Val de Saône watershed between the 1980s and 2000. The results obtained from three coarse resolution series, NOAA/AVHRR, SPOT/VGT-S10 and SPOT/VGT-P, have been compared.     

Granularity in all-optical WDM networks for large geographical areas

Granularity is a key issue in the design of an optical layer of a transport network. In fact, the choices of the number of wavelength division multiplexing (WDM) channels and of the bit rate of each channel have a significant impact on the design of the network nodes, and influence both the network management and the transmission performance. In a recent paper we analyzed this issue, assuming no-regeneration along the transmission path through the network, providing simulation results in some relevant cases. In this paper, we report results of the analysis extended to large geographical areas, where the transmission spans can be long enough to give rise to further transmission impairments due to nonlinear effects during fiber propagation. In particular, the transmission performance for all-optical meshed network, with several OXC crossed, is studied considering different link capacities.     

Fast power estimation of large circuits

Our new technique for estimating transition probabilities of internal signals in combinational circuits uses Markov chains and reconvergence regions. To efficiently implement the computation, we use ROBDDs (reduced, ordered binary decision diagrams). Accounting for temporal dependence of signals, multiple concurrent transitions, and mutual dependence of internal signals, the technique provides an exact computation for small circuits and an approximate estimate for large circuits. Experimental results show the estimation technique is fast with only small inaccuracies     

Operational modal parameter estimation of MIMO systems using transmissibility functions

Operational modal parameter estimation (OMA) techniques perform system identification without or with only limited knowledge of the operational inputs acting on the system. However, most of the current operational identification techniques impose multiple conditions on the spectral content of the unknown inputs. As a consequence, modeling errors occur if these assumptions are not met. Therefore, there is a general interest in operational identification techniques that can operate independent of the unknown input spectra.     

Performance of AERMOD at different time scales

As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be verified. This paper evaluates the performance of AERMOD in estimating sulfur dioxide (SO₂) concentrations in Dallas and Ellis counties in Texas. Results suggest that SO₂ concentrations simulated by AERMOD at the 8 h, daily, monthly, and annual intervals match their respective observed concentrations much better compared with the simulated 1 and 3 h SO₂ concentrations. In addition, AERMOD performs better in simulating SO₂ concentrations when combined point and mobile emission sources are used as model inputs rather than using point or mobile emission sources alone. Results also suggest that, at the monthly scale, AERMOD performs much better in simulating the high end of the spectrum of SO₂ concentrations in the study area compared to results at the 1, 3, 8 h, and daily scales. These results not only help us better understand the performance of AERMOD but also provide useful information to researchers who are interested in applying AERMOD in various applications, such as the utilization of AERMOD in chronic exposure assessment in epidemiological studies where long-term (i.e., monthly and/or annual) air pollution concentration estimations are often used.     

Trajectory estimation algorithm for mobile nodes using encounter information and geographical information

In this paper, we propose a centralized algorithm to estimate trajectories of mobile nodes using a reasonable number of landmarks, ad hoc wireless communication facility and geographical information. The method identifies such trajectories where nodes move between two landmarks with the shortest time, which may be the shortest paths with high possibility, and uses their intermediate positions as “quasi-landmarks” to localize the others. The method can deal with a realistic case where mobile nodes move along pathways among obstacles. From simulation results, we show that the average estimation error is less than 40% of the wireless range under various scenarios.     

Operational flexibility and optimal total production cost in multipleitem economic production quantity models

This study investigates the problems of operational flexibility, optimal production rate and production runs in multiple-item economic production quantity models under a rotation cycle basis. The production rate of each item and the system rotation cycle are crucial decision variables in this study. The unit production cost of each item is a function of its production rate. The relationship between the optimal total production cost and operational flexibility, which is affected by the unit production cost function of each item, is analysed. Sensitivity analyses have been conducted to evaluate the effects of operational flexibility on the individual parameters of the unit production cost function. An industrial case has also been presented as an illustration.     

Suboptimal estimation of systems with large parameter uncertainties

The design of a filter that must estimate the state of a system when large parameter uncertainties in the plant dynamics or plant and measurement noise covariance matrices are present is considered. Filter designs are evaluated relative to the trade off between filter sensitivity to the uncertain parameters and minimum mean-square estimation error. This is accomplished by reformulating the estimation problem as an optimization problem with vector-valued performance index. The dominant design has form similar to the Kalman-Bucy filter, and employ open-loop compensation for the uncertain parameters.     

Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristics

The goal of the current study was to develop methods of estimating the height of vertical components within plantation coniferous forest using airborne discrete multiple return lidar. In the summer of 2008, airborne lidar and field data were acquired for Loblolly pine forest locations in North Carolina and Virginia, USA, which comprised a variety of stand conditions (e.g. stand age, nutrient regime, and stem density). The methods here implement both field plot-scale analysis and an automated approach for the delineation of individual tree crown (ITC) locations and horizontal extents through a marker-based region growing process applied to a lidar derived canopy height model. The estimation of vertical features was accomplished through aggregating lidar return height measurements into vertical height bins, of a given horizontal extent (plot or ITC), creating a vertical ‘stack’ of bins describing the frequency of returns by height. Once height bins were created the resulting vertical distributions were smoothed with a regression curve-line function and canopy layers were identified through the detection of local maxima and minima. Estimates from Lorey’s mean canopy height was estimated from plot-level curve-fitting with an overall accuracy of 5.9% coefficient of variation (CV) and the coefficient of determination (R²) value of 0.93. Estimates of height to the living canopy produced an overall R² value of 0.91 (11.0% CV). The presence of vertical features within the sub-canopy component of the fitted vertical function also corresponded to areas of known understory presence and absence. Estimates from ITC data were averaged to the plot level. Estimates of field Lorey’s mean canopy top height from average ITC data produced an R² value of 0.96 (7.9% CV). Average ITC estimates of height to the living canopy produced the closest correspondence to the field data, producing an R² value of 0.97 (6.2% CV). These results were similar to estimates produced by a statistical regression method, where R² values were 0.99 (2.4% CV) and 0.98 (4.9% CV) for plot average top canopy height and height to the living canopy, respectively. These results indicate that the characteristics of the dominant canopy can be estimated accurately using airborne lidar without the development of regression models, in a variety of intensively managed coniferous stand conditions.     

Factor-GMM estimation with large sets of possibly weak instruments

The use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity is analysed. In particular, the focus is on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and empirical applications highlight the relevance of Factor-GMM estimation, which is also easily implemented.     

多时间尺度时间序列趋势预测

针对股票、基金等大量时间序列数据的趋势预测问题，提出一种基于新颖特征模型的多时间尺度时间序列趋势预测算法。首先，在原始时间序列中提取带有多时间尺度特征的特征树，其刻画了时间序列，不仅带有序列在各个层次的特征，同时表示了层次之间的关系。然后，利用聚类挖掘特征序列中的隐含状态。最后，应用隐马尔可夫模型（HMM）设计一个多时间尺度趋势预测算法（MTSTPA），同时对不同尺度下的趋势以及趋势的长度作出预测。在真实股票数据集上的实验中，在各个尺度上的预测准确率均在60%以上，与未使用特征树对比，使用特征树的模型预测效率更高，在某一尺度上准确率高出10个百分点以上。同时，与经典自回归滑动平均模型（ARMA）模型和PHMM（Pattern-based HMM）对比，MTSTPA表现更优，验证了其有效性。     

Satellite remote sensing of primary production

Leaf structure and function are shown to result in distinctive variations in the absorption and reflection of solar radiation from plant canopies. The leaf properties that determine the radiation-interception characteristics of plant canopies are directly linked to photosynthesis, stomatal resistance and evapotran-spiration and can be inferred from measurements of reflected solar energy. The effects of off-nadir viewing and atmospheric constituents, coupled with the need to measure changing surface conditions, emphasize the need for multitemporal measurements of reflected radiation if primary production is to be estimated