基于Vega的直升机视景仿真技术研究与实现

以开发一套直升机飞行模拟仿真系统为目的,对仿真应用系统开发中各种常见的建模和驱动技术尤其是动态仪表面板仿真模块进行了研究,提出了可行的实现方案;利用建模工具Creator对飞机、仪表和大面积地形进行了可视化建模,利用DOF(Degrees OfFreedom)技术实现了直升机旋翼、尾翼以及仪表指针的动态交互;基于VC++集成开发环境,应用Vega API编程技术实现了直升机视景仿真应用系统的全部设计功能;直升机试飞实验证明,该视景系统取得了比传统视景系统更好的效果。     

Correctness of belief propagation in Gaussian graphical models of arbitrary topology

Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. Local "belief propagation" rules of the sort proposed by Pearl (1988) are guaranteed to converge to the correct posterior probabilities in singly connected graphs. Recently, good performance has been obtained by using these same rules on graphs with loops, a method we refer to as loopy belief propagation. Perhaps the most dramatic instance is the near Shannon-limit performance of "Turbo codes," whose decoding algorithm is equivalent to loopy propagation. Except for the case of graphs with a single loop, there has been little theoretical understanding of loopy propagation. Here we analyze belief propagation in networks with arbitrary topologies when the nodes in the graph describe jointly gaussian random variables. We give an analytical formula relating the true posterior probabilities with those calculated using loopy propagation. We give sufficient conditions for convergence and show that when belief propagation converges, it gives the correct posterior means for all graph topologies, not just networks with a single loop. These results motivate using the powerful belief propagation algorithm in a broader class of networks and help clarify the empirical performance results.     

An Evaluation of Explanations of Probabilistic Inference

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.     

A framework for controlling model-based diagnosis systems with multiple actions

In recent years reasoning about structure and function of physical systems for the purpose of diagnosis has seen a dramatic increase in activities. New exciting results concerning modelling issues, diagnostic inference patterns and inferential power have emerged. A state of the art diagnosis agent now has a considerable toolset at hand. A main obstacle for building large diagnosis systems, however, remains. How can we controlwhen to usewhich inference pattern or representation? We argue that the actions available to a diagnosis agent can be understood in terms of change ofworking hypotheses. The control problem then becomes a belief revision problem: when to adopt or drop beliefs. Our approach proceeds in two steps. First, we adopt the principle of informational economy from Gärdenfors, Knowledge in Flux (MIT Press, 1988) as kind of a law of inertia for diagnostic processes, that helps us identify candidates for revised belief states. In a second step we employ specificdiagnostic knowledge to actually choose the next belief state. We demonstrate the use of our concepts on an example in the domain of ballast tank systems as e.g. used in offshore plants.     

Stochastic reasoning, free energy, and information geometry

Belief propagation (BP) is a universal method of stochastic reasoning. It gives exact inference for stochastic models with tree interactions and works surprisingly well even if the models have loopy interactions. Its performance has been analyzed separately in many fields, such as AI, statistical physics, information theory, and information geometry. This article gives a unified framework for understanding BP and related methods and summarizes the results obtained in many fields. In particular, BP and its variants, including tree reparameterization and concave-convex procedure, are reformulated with information-geometrical terms, and their relations to the free energy function are elucidated from an information-geometrical viewpoint. We then propose a family of new algorithms. The stabilities of the algorithms are analyzed, and methods to accelerate them are investigated.     

Physics-based modeling strategies for diagnostic and prognostic application in aerospace systems

This paper presents physics-based models as a key component of prognostic and diagnostic algorithms of health monitoring systems. While traditionally overlooked in condition-based maintenance strategies, these models potentially offer a robust alternative to experimental or other stochastic modeling data. Such a strategy is particularly useful in aerospace applications, presented in this paper in the context of a helicopter transmission model. A lumped parameter, finite element model of a widely used helicopter transmission is presented as well as methods of fault seeding and detection. Fault detection through diagnostic vibration parameters is illustrated through the simulation of a degraded rolling-element bearing supporting the transmission’s input shaft. Detection in the time domain and frequency domain is discussed. The simulation shows such modeling techniques to be useful tools in health monitoring analysis, particularly as sources of information for algorithms to compare with real-time or near real-time sensor data.     

Grammatical inference in bioinformatics

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.     

Model Failure and Context Switching Using Logic-Based Stochastic Models

This paper addresses parameter drift in stochastic models. We define a notion of context that represents invariant, stable-over-time behavior and we then propose an algorithm for detecting context changes in processing a stream of data. A context change is seen as model failure, when a probabilistic model representing current behavior is no longer able to “fit” newly encountered data. We specify our stochastic models using a first-order logic-based probabilistic modeling language called Generalized Loopy Logic (GLL). An important component of GLL is its learning mechanism that can identify context drift. We demonstrate how our algorithm can be incorporated into a failure-driven context-switching probabilistic modeling framework and offer several examples of its application.     

Parallel communicating grammar systems with terminal transmission

We introduce a new variant of PC grammar systems, called PC grammar systems with terminal transmission, PCGSTT for short. We show that right-linear centralized PCGSTT have nice formal language theoretic properties: they are closed under gsm mappings (in particular, under intersection with regular sets and under homomorphisms) and union; a slight variant is, in addition, closed under concatenation and star; their power lies between that of n-parallel grammars introduced by Wood and that of matrix languages of index n, and their relation to equal matrix grammars of degree n is discussed. We show that membership for these language classes is complete for NL. In a second part of the paper, we discuss questions concerning grammatical inference of these systems. More precisely, we show that PCGSTT whose component grammars are terminal distinguishable right-linear, a notion introduced by Radhakrishnan and Nagaraja in [33,34], are identifiable in the limit if certain data communication information is supplied in addition.     

用于虚拟广告系统的视频对象分割算法

在虚拟广告系统中,视频对象分割是其中最为关键的技术之一。在兼顾分割精度和实时性的原则上,提出了一种基于置信传播的视频运动对象分割算法。算法先建立背景、阴影和前景的统计模型,再结合马尔可夫随机场对像素空间相关性建模,最后利用置信传播算法完成有效的视频对象分割。实验结果表明算法具有良好的性能,并在虚拟广告系统中得到成功应用。     

一种通用直升机飞行动力学建模软件

直升机因为特有的旋翼气动特性,使得其飞行建模较为复杂,而实时仿真系统的建立在飞机研制、性能验证及改型等各环节中都起到了一个重要的作用.文章介绍了一个单旋翼带尾桨直升机通用的建模工具,在此工具下,只需要输入直升机的构型参数以及相应的风洞气动数据,就可以建立起直升机的全量非线性动力学数学模型,并提供相应的配平程序以及动态响应计算程序.相比较于国外同类型的软件,文中提出的建模工具建立的模型可以运行在实时操作系统上.     

From machine learning to machine reasoning

A plausible definition of “reasoning” could be “algebraically manipulating previously acquired knowledge in order to answer a new question”. This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labelled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated “all-purpose” inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

Neighborhood-consensus message passing as a framework for generalized iterated conditional expectations

In this paper we propose a novel inference method for maximum a posteriori estimation with Markov random field prior. The central idea is to integrate a kind of joint “voting” of neighboring labels into a message passing scheme similar to loopy belief propagation (LBP). While the LBP operates with many pairwise interactions, we formulate “messages” sent from a neighborhood as a whole. Hence the name neighborhood-consensus message passing (NCMP). The practical algorithm is much simpler than LBP and combines the flexibility of iterated conditional modes (ICM) with some ideas of more general message passing. The proposed method is also a generalization of the iterated conditional expectations algorithm (ICE): we revisit ICE and redefine it in a message passing framework in a more general form. We also develop a simplified version of NCMP, called weighted iterated conditional modes (WICM), that is suitable for large neighborhoods. We verify the potentials of our methods on four different benchmarks, showing the improvement in quality and/or speed over related inference techniques.     

ORGANIZATIONAL MEMORY: THREE EXPERIMENTS ON THE QUALITY OF INFORMATION

Abstract

An expert system in ethical organizational administration is a new and appropriate venture for workers in artificial intelligence. The positive relationship between knowledge and belief along with the inextricable connection of fact to value set the general systems design for this modeling process. The premises of general systems theory dictate the modeling of an holistic ethical system. The pattern of the holistic ethical system of Orthodox Christianity is used to design the flow diagram of the decision-making and judgment-making processes in ethical thought. There are seven symbolic propositions that detail these ethical processes. With the public language of the United States being secular, four Orthodox Christian ethical principles were transformed from their biblical and theological language into four university ethical policies using secular language. The writer's future design of computer software will confirm or deny the wisdom of this approach to modeling an ethical system.     

直升机飞行动力学建模及可视化研究

研究了直升机飞行动力学建模及可视化仿真的相关技术。建模时综合考虑了直升机的主要气动力部件的建模以及气动干扰等问题。主旋翼是直升机建模的关键,详细讨论了与其相关的翼型气动力模型、诱导速度模型和挥舞运动模型的建模方法。同时利用虚拟现实技术构建了直升机三维实体模型。基于Simulink和虚拟现实工具箱,给出了将飞行动力学数值模型同虚拟场景结合起来的方法,实现了计算、仿真、显示一体化。最后采用算例直升机数据,进行了定直飞行的配平计算和操纵响应分析,结果证明了模型的合理性和有效性。     

Inclusion dynamics hybrid automata

Hybrid systems are dynamical systems with the ability to describe mixed discrete-continuous evolution of a wide range of systems. Consequently, at first glance, hybrid systems appear powerful but recalcitrant, neither yielding to analysis and reasoning through a purely continuous-time modeling as with systems of differential equations, nor open to inferential processes commonly used for discrete state-transition systems such as finite state automata. A convenient and popular model, called hybrid automata, was introduced to model them and has spurred much interest on its tractability as a tool for inference and model checking in a general setting. Intuitively, a hybrid automaton is simply a “finite-state” automaton with each state augmented by continuous variables, which evolve according to a set of well-defined continuous laws, each specified separately for each state. This article investigates both the notion of hybrid automaton and the model checking problem over such a structure. In particular, it relates first-order theories and analysis results on multivalued maps and reduces the bounded reachability problem for hybrid automata whose continuous laws are expressed by inclusions (x′f(x,t)) to a decidability problem for first-order formulæ over the reals. Furthermore, the paper introduces a class of hybrid automata for which the reachability problem can be decided and shows that the problem of deciding whether a hybrid automaton belongs to this class can be again decided using first-order formulæ over the reals. Despite the fact that the bisimulation quotient for this class of hybrid automata can be infinite, we show that our techniques permit effective model checking for a nontrivial fragment of CTL.     

Using semantic analysis to improve speech recognition performance

Although syntactic structure has been used in recent work in language modeling, there has not been much effort in using semantic analysis for language models. In this study, we propose three new language modeling techniques that use semantic analysis for spoken dialog systems. We call these methods concept sequence modeling, two-level semantic-lexical modeling, and joint semantic-lexical modeling. These models combine lexical information with varying amounts of semantic information, using annotation supplied by either a shallow semantic parser or full hierarchical parser. These models also differ in how the lexical and semantic information is combined, ranging from simple interpolation to tight integration using maximum entropy modeling. We obtain improvements in recognition accuracy over word and class N-gram language models in three different task domains. Interpolation of the proposed models with class N-gram language models provides additional improvement in the air travel reservation domain. We show that as we increase the semantic information utilized and as we increase the tightness of integration between lexical and semantic items, we obtain improved performance when interpolating with class language models, indicating that the two types of models become more complementary in nature.     

Bootstrapping Inference in the IDP Knowledge Base System

Declarative systems aim at solving tasks by running inference engines on a specification, to free their users from having to specify how a task should be tackled. In order to provide such functionality, declarative systems themselves apply complex reasoning techniques, and, as a consequence, the development of such systems can be laborious work. In this paper, we demonstrate that the declarative approach can be applied to develop such systems, by tackling the tasks solved inside a declarative system declaratively. In order to do this, a meta-level representation of those specifications is often required. Furthermore, by using the language of the system for the meta-level representation, it opens the door to bootstrapping: an inference engine can be improved using the inference it performs itself.One such declarative system is the IDP knowledge base system, based on the language \(\rm FO(\cdot)^{\rm IDP}\), a rich extension of first-order logic. In this paper, we discuss how \(\rm FO(\cdot)^{\rm IDP}\) can support meta-level representations in general and which language constructs make those representations even more natural. Afterwards, we show how meta-\(\rm FO(\cdot)^{\rm IDP}\) can be applied to bootstrap its model expansion inference engine. We discuss the advantages of this approach: the resulting program is easier to understand, easier to maintain, and more flexible.     

The unified extreme learning machines and discriminative random fields for automatic knee cartilage and meniscus segmentation from multi-contrast MR images

Segmenting articular cartilage and meniscus from magnetic resonance (MR) images is an essential task for the assessment of knee pathology. Most of the previous classification-based works for cartilage and meniscus segmentation only rely on independent labellings by a classifier, but do not consider the spatial context interaction. The labels of most image voxels are actually dependent upon their neighbours. In this study, we present an automatic knee segmentation system working on multi-contrast MR images where a novel classification model unifying an extreme learning machine (ELM)-based association potential and a discriminative random field (DRF)-based interaction potential is proposed. The DRF model introduces spatial dependencies between neighbouring voxels to the independent ELM classification. We exploit a rich set of features From multi-contrast MR images to train the proposed classification model and perform the loopy belief propagation for the inference. The proposed model is evaluated on multi-contrast MR datasets acquired from 11 subjects with results outperforming the independent classifiers in terms of segmentation accuracy of both cartilages and menisci.