基于多模型假设检验的认知无线电频谱感知方法

提出一种基于序贯概率似然比多模型假设检验的认知无线电协作频谱感知方法,用于检测可能含有不同结构和参数不确定性的未知信号.传统的认知无线电协作频谱感知方法(如基于序贯概率似然比的单模型假设检验、M元假设检验等),仅限于处理已知信号分布,不考虑信号分布的不确定性,可能会造成检测误判.所提出方法不仅可以处理认知无线电信号分布模型的不确定性问题,而且可以得到满足错误概率约束的有效检测.对频谱感知的一个典型场景进行仿真实验,结果表明所提出基于序贯概率似然比多模型假设检验方法相对于传统方法的检测有效性.     

Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.     

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.     

Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem

Estimation of canopy biophysical variables from remote sensing data was investigated using radiative transfer model inversion. Measurement and model uncertainties make the inverse problem ill posed, inducing difficulties and inaccuracies in the search for the solution. This study focuses on the use of prior information to reduce the uncertainties associated to the estimation of canopy biophysical variables in the radiative transfer model inversion process. For this purpose, lookup table (LUT), quasi-Newton algorithm (QNT), and neural network (NNT) inversion techniques were adapted to account for prior information. Results were evaluated over simulated reflectance data sets that allow a detailed analysis of the effect of measurement and model uncertainties. Results demonstrate that the use of prior information significantly improves canopy biophysical variables estimation. LUT and QNT are sensitive to model uncertainties. Conversely, NNT techniques are generally less accurate. However, in our conditions, its accuracy is little dependent significantly on modeling or measurement error. We also observed that bias in the reflectance measurements due to miscalibration did not impact very much the accuracy of biophysical estimation.     

Random matrix theory for modeling uncertainties in computational mechanics

This paper deals with data uncertainties and model uncertainties issues in computational mechanics. If data uncertainties can be modeled by parametric probabilistic methods, for a given mean model, a nonparametric probabilistic approach can be used for modeling model uncertainties. The first part is devoted to random matrix theory for which we summarize previous published results and for which two new ensembles of random matrices useful for the nonparametric models are introduced. In a second part, the nonparametric probabilistic approach of random uncertainties is presented for linear dynamical systems and for nonlinear dynamical systems constituted of a linear part with additional localized nonlinearities. In a third part, a new method is proposed for estimating the parameters of the nonparametric approach from experiments. Finally, examples with experimental comparisons are given.     

Adaptive Robust Control for a Lower Limbs Rehabilitation Robot Running Under Passive Training Mode

This paper focuses on the problem of the adaptive robust control of a lower limbs rehabilitation robot (LLRR) that is a nonlinear system running under passive training mode. In reality, uncertainties including modeling error, initial condition deviation, friction force and other unknown external disturbances always exist in a LLRR system. So, it is necessary to consider the uncertainties in the unilateral man-machine dynamical model of the LLRR we described. In the dynamical model, uncertainties are (possibly fast) time-varying and bounded. However, the bounds are unknown. Based on the dynamical model, we design an adaptive robust control with an adaptive law that is leakage type based and on the framework of Udwadia-Kalaba theory to compensate for the uncertainties and to realize tracking control of the LLRR. Furthermore, the effectiveness of designed control is shown with numerical simulations.      

Multisensor-based object identification using uncertain geometric models

One of the problems in sensor integration is how to design the integration strategy for the given task. In this paper, we deal with model-based object recognition from uncertain geometric observations using uncertain object models. First, we decompose the recognition problem into a hierarchy of statistically well-defined subproblems depending on sensor uncertainties and model uncertainties. A recognition algorithm based on this approach is developed. Second, a method to preserve the consistency under model uncertainties is discussed. It is shown that information loss can be avoided by adding dummy variables to parameters in the integration. Finally, applications of the proposed method to two-dimensional object recognition are demonstrated.     

遥感数据在水文模拟中的应用研究进展

水文模型已成为水文研究中不可或缺的重要部分。水文模型在经过黑箱模型阶段和集总式模型阶段后,现已发展到分布式水文模型阶段。相比于传统的站点观测,遥感技术由于能够获取面源信息、资料相对易获取并有稳定的较长时序观测等优势在水文模型中获得越来越广泛的应用。同时,近年来遥感数据产品在观测能力、可靠性和准确性、多源融合技术、卫星组网技术等方面取得了长足进展。遥感数据在水文模型中的应用主要有获取驱动数据,获取参数和边界条件,获取状态变量等3种方式。近些年来由于数据同化技术的发展,通过同化遥感观测数据改善水文模型模拟结果已成为遥感数据在水文应用中的一大研究热点。当前,遥感观测仍存在不确定性较高、时空尺度问题、瞬时观测、难以获取深层土壤信息等问题需要克服,如何在水文模型中充分利用不同来源、不同尺度的观测数据将是未来水文学研究的一个重要方向。     

The complexity of planar compliant motion planning under uncertainty

We consider the computational complexity of planning compliant motions in the plane, given geometric bounds on the uncertainty in sensing and control. We can give efficient algorithms for generating and verifying compliant motion strategies that are guaranteed to succeed as long as the sensing and control uncertainties lie within the specified bounds. We also consider the case where a compliant motion plan is required to succeed over some parametric family of geometries. While these problems are known to be intractable in three dimensions, we identify tractable subclasses in the plane.     

积雪属性非均匀性对被动微波遥感积雪的影响

积雪属性的非均匀性在水平方向上表现为像元内积雪未完全覆盖和雪深分布的不均匀,在垂直方向上表现为积雪剖面上粒径和密度的不一致导致的积雪分层现象。这些积雪属性的非均匀性对被动微波遥感反演雪深或雪水当量带来很大的不确定性,并且给反演结果的验证带来不确定性。通过野外积雪的微波辐射特性观测、遥感积雪产品对比分析、积雪辐射传输模型模拟对这些问题进行阐述和探讨,为今后积雪微波遥感反演算法发展和结果验证提供参考。     

Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds

With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model.     

Multi-agent coverage control for enhanced geohazard monitoring: a brief review

In recent years, cooperative coverage control of multi-agent system (MAS) has attracted plenty of researchers in various fields [1, 2]. Different from multi-agent consensus or synchronization, multi-agent coverage control cares about how to coordinate a team of agents for effectively monitoring or covering a given terrain, which inevitably gives rise to the interaction between individual dynamics and external environments. Nevertheless, environmental uncertainties that include static uncertainties and dynamic uncertainties and limited sensing capabilities of a single agent make it a great challenge to design control algorithms of MAS for achieving the desired coverage performance....     

Robot Motion Planning: A Game-Theoretic Foundation

Analysis techniques and algorithms for basic path planning have become quite valuable in a variety of applications such as robotics, virtual prototyping, computer graphics, and computational biology. Yet, basic path planning represents a very restricted version of general motion planning problems often encountered in robotics. Many problems can involve complications such as sensing and model uncertainties, nonholonomy, dynamics, multiple robots and goals, optimality criteria, unpredictability, and nonstationarity, in addition to standard geometric workspace constraints. This paper proposes a unified, game-theoretic mathematical foundation upon which analysis and algorithms can be developed for this broader class of problems, and is inspired by the similar benefits that were obtained by using unified configuration-space concepts for basic path planning. By taking this approach, a general algorithm has been obtained for computing approximate optimal solutions to a broad class of motion planning problems, including those involving uncertainty in sensing and control, environment uncertainties, and the coordination of multiple robots. Received November 11, 1996; revised March 13, 1998.     

A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery

Identifying buildings from remote sensing imagery has been a challenge due to uncertainties from remote sensing imagery and variations in building structure and texture. In this study, we develop a scale robust CNN structure to improve the segmentation accuracy of building data from high-resolution aerial and satellite images. Based on a fully convolutional network, we introduce two Atrous convolutions on the first two lowest-scale layers, respectively, in the decoding step, aiming at enlarging the sight-of-view and integrate semantic information of large buildings. Then, a multi-scale aggregation strategy is applied. The last feature maps of each scale are used to predict the corresponding building labels, and further up-sampled to the original scale and concatenated for the final prediction. In addition, we introduce a combined data augmentation and relative radiometric calibration method for multi-source building extraction. The method enlarges sample spaces and hence the generalization ability of the deep learning models. We validate our developed methods with an aerial dataset of more than 180, 000 buildings with various architectural types, and a satellite image dataset consists of more than 29,000 buildings. The results are compared with several most recent studies. The comparison result shows our neural network outperformed other studies, especially in segmenting scenes of large buildings. The test on transfer learning from aerial dataset to satellite dataset showed our augmentation strategy significantly improved the prediction accuracy; however, further studies are needed to improve the generalization ability of the CNN model.     

Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

This paper presents two intelligent adaptive controllers, called self‐balancing and speed controllers, for self‐balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self‐balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding‐mode controller with online learning FBFN is proposed to accomplish self‐balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory‐built electric unicycle.     

Backstepping-based synchronisation of uncertain networked Lagrangian systems

In this article, we study the synchronisation problem of uncertain networked Lagrangian systems on directed communication topologies. For the nominal model without uncertainties, we propose a backstepping-based synchronisation design for heterogenous Lagrangian systems on directed graphs with a spanning tree. We relax earlier constraints on the feedback gain for the distributed synchronisation control law, which encompasses the existing double integrator consensus problem as a special case. We then extend the proposed design to the case without relative velocity measurement. For the uncertain Lagrangian model, we develop a distributed adaptive redesign so that asymptotic synchronisation convergence is achieved in the presence of linearly parameterised model uncertainties. Simulation results show the effectiveness of the proposed method.     

On the learning of rule uncertainties and their integration into probabilistic knowledge bases

We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules that he thinks are good indicators of whether a specific target concept is likely to occur. In a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Then, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information.Formally, we introduce the natural class of disjunctive probabilistic concepts and prove that this class is efficiently distribution-free learnable. The distribution-free learning model of probabilistic concepts was introduced by Kearns and Schapire and generalizes Valiant's probably approximately correct learning model. We show how to simulate the learned concepts in probabilistic knowledge bases which satisfy the laws of axiomatic probability theory. Finally, we combine the rule uncertainties with uncertain facts and prove the correctness of the combination under an independence assumption.     

Digital self-triggered robust control of nonlinear systems

In this paper, we develop novel results on self-triggered control of nonlinear systems, subject to perturbations, and sensing/computation/actuation delays. First, considering an unperturbed nonlinear system with bounded delays, we provide conditions that guarantee the existence of a self-triggered control strategy stabilizing the closed-loop system. Then, considering parameter uncertainties, disturbances and bounded delays, we provide conditions guaranteeing the existence of a self-triggered strategy that keeps the state arbitrarily close to the equilibrium point. In both cases, we provide a methodology for the computation of the next execution time. We show on an example the relevant benefits obtained with this approach in terms of energy consumption with respect to control algorithms based on a constant sampling with a sensible reduction of the average sampling time.     

Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; (R_0.86 − R_1.24) / (R_0.86 + R_1.24)) and NDII (Normalized Difference Infrared Index; (R_0.86 − R_1.64) / (R_0.86 + R_1.64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (< 100), retrieval errors could lead to normalized spread rate errors of 6.5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC < 175, LFMC retrieval errors could amount to normalized spread rate errors of 0.6 or an equivalent error of 9.3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions.     

Performance analysis of a hybrid system under quality impact of returns

In this paper we analyze a hybrid system that meets the demand with remanufactured or new products. In the remanufacturing stage there are uncertainties in the quality of remanufactured products, return rates and return times of returned products. These uncertainties affect raw material order quantities, processing times and material recovery rates. In the study returned products are classified by considering quality uncertainties. According to this classification remanufacturing processing times, material recovery rates, remanufacturing costs and disposal costs are determined. In order to analyze the effect of uncertainties in return quality a simulation model is constructed by using the ARENA simulation program. Our analysis denotes that under different cost scenarios quality based classification of returned products brings significant cost savings. The numerical analysis indicates that a cost improvement of more than 8% is achieved when return rates are high.