Transputer网络上分布式组合搜索的效率

Ｔｒａｎｓｐｕｔｅｒ网络是一种典型的消息传送型多处理机系统，无论是传统的数值计算，还是人工智能的动态非确定搜索问题，都可以得到加速处理。本文研究如何在Ｔｒａｎｓｐｕｔｅｒ网络上高效地实现分布式组合搜索。针对搜索的动态非确定性，我们提出一种异步通讯模式，以及Ｔｒａｎｓｐｕｔｅｒ上并行搜索的负载平衡算法。我们实现了最佳优先和启发式导向的深度优先两种策略的分布式分枝限界算法（Ｂｒａｎｃｈ－ａｎｄ－Ｂｏｕｎｄ），应用于求解旅行推销员问题（ＴＳＰ），在１６个Ｔｒａｎｓｐｕｔｅｒ上获得了较好的并行效率。     

Combining evidence with a probabilistic framework for answer ranking and answer merging in question answering

Question answering (QA) aims at finding exact answers to a user’s question from a large collection of documents. Most QA systems combine information retrieval with extraction techniques to identify a set of likely candidates and then utilize some ranking strategy to generate the final answers. This ranking process can be challenging, as it entails identifying the relevant answers amongst many irrelevant ones. This is more challenging in multi-strategy QA, in which multiple answering agents are used to extract answer candidates. As answer candidates come from different agents with different score distributions, how to merge answer candidates plays an important role in answer ranking. In this paper, we propose a unified probabilistic framework which combines multiple evidence to address challenges in answer ranking and answer merging. The hypotheses of the paper are that: (1) the framework effectively combines multiple evidence for identifying answer relevance and their correlation in answer ranking, (2) the framework supports answer merging on answer candidates returned by multiple extraction techniques, (3) the framework can support list questions as well as factoid questions, (4) the framework can be easily applied to a different QA system, and (5) the framework significantly improves performance of a QA system. An extensive set of experiments was done to support our hypotheses and demonstrate the effectiveness of the framework. All of the work substantially extends the preliminary research in Ko et al. (2007a). A probabilistic framework for answer selection in question answering. In: Proceedings of NAACL/HLT.     

基于系统动力学的山西省能源消费可持续发展研究

能源是国民经济发展的物质基础,现代社会发展对能源消费依赖程度日益提高。能源消费系统作为复杂系统,与经济、人口、环境等外在因素紧密相连,认识和把握能源消费规律及其变化趋势成为提高能源安全的基本前提和必要条件。本文选取能源大省山西作为研究对象。山西省既是能源生产大省,也是能源消费大省,高耗能的产业结构、初级能源为主的品种构成,导致山西的经济发展是以消耗大量的能源为代价的。论文运用系统工程的思想,对山西省能源消费系统及其相关子系统进行分析,构建系统动力学模型,采用5种不同发展模式分别对模型中节能技术、洗煤率、SO2排放系数等关键因子进行调控,实现对2010年-2020年间GDP增长率、单位GDP能耗和SO2排放总量的中长期预测。对比5种发展模式的预测结果及变化趋势,并结合山西省的实际情况,认为模式5能够较好实现能源消费与经济增长协调发展,是相对最优调控方案。该方案给出的政策建议包括:加强现有装备技术改造,进一步关停落后产能,加强资源循环利用,削减污染物前端排放,加快发展太阳能、沼气等新能源等。     

Decentralised delay-dependent static output feedback variable structure control

In this paper, an output feedback stabilisation problem is considered for a class of large scale interconnected time delay systems with uncertainties. The uncertainties appear in both isolated subsystems and interconnections. The bounds on the uncertainties are nonlinear and time delayed. It is not required that either the known interconnections or the uncertain interconnections are matched. Then, a decentralised delay-dependant static output feedback variable structure control is synthesised to stabilise the system globally uniformly asymptotically using the Lyapunov Razumikhin approach. A case study relating to a river pollution control problem is presented to illustrate the proposed approach.     

Finite-time stabilization of nonlinear dynamical systems via control vector Lyapunov functions

Finite-time stability involves dynamical systems whose trajectories converge to an equilibrium state in finite time. Since finite-time convergence implies nonuniqueness of system solutions in reverse time, such systems possess non-Lipschitzian dynamics. Sufficient conditions for finite-time stability have been developed in the literature using Hölder continuous Lyapunov functions. In this paper, we develop a general framework for finite-time stability analysis based on vector Lyapunov functions. Specifically, we construct a vector comparison system whose solution is finite-time stable and relate this finite-time stability property to the stability properties of a nonlinear dynamical system using a vector comparison principle. Furthermore, we design a universal decentralized finite-time stabilizer for large-scale dynamical systems that is robust against full modeling uncertainty. Finally, we present two numerical examples for finite-time stabilization involving a large-scale dynamical system and a combustion control system.     

嵌入式网关中分布式通信模块设计要点分析

针对具有分布式体系结构的嵌入式网关,分析和研究了面向网络应用的分布式通信模块的设计思想,讨论了基于消息传递的分布式通信模块相关的设计要点,为基于消息传递的分布式系统的通信模块的设计和实现在设计思想及理论上提供支持。     

基于UML的电梯控制系统的分析与设计

UML是一种基于面向对象的可视化建模语言。本文简要介绍了UML的建模机制并将电梯管理系统作为实例,运用UML语言的图形表达方式,从需求分析到详细设计,及类与类之间的消息传递关系,给出了该方法的应用细节,探索了基于UML的应用领域的系统开发及UML各图形之间的内在联系,说明了应用UML语言丰富的可视化表达元素,可以清晰、快速地定义面向对象的软件系统模型。     

Probabilistic fuzzy logic controller for uncertain nonlinear systems

This paper proposes a probabilistic fuzzy proportional - integral (PFPI) controller for controlling uncertain nonlinear systems. Firstly, the probabilistic fuzzy logic system (PFLS) improves the capability of the ordinary fuzzy logic system (FLS) to overcome various uncertainties in the controlled dynamical systems by integrating the probability method into the fuzzy logic system. Moreover, the input/output relationship for the proposed PFPI controller is derived. The resulting structure is equivalent to nonlinear PI controller and the equivalent gains for the proposed PFPI controller are a nonlinear function of input variables. These gains are changed as the input variables changed. The sufficient conditions for the proposed PFPI controller, which achieve the bounded-input bounded-output (BIBO) stability are obtained based on the small gain theorem. Finally, the obtained results indicate that the PFPI controller is able to reduce the effect of the system uncertainties compared with the fuzzy PI (FPI) controller.     

Steady-state analysis of probabilistic Boolean networks

This paper investigates steady-state distributions of probabilistic Boolean networks via cascading aggregation. Under this approach, the problem is converted to computing least square solutions to several corresponding equations. Two necessary and sufficient conditions for the existence of the steady-state distributions for probabilistic Boolean networks are given firstly. Secondly, an algorithm for finding the steady-state distributions of probabilistic probabilistic Boolean networks is given. Finally, a numerical example is given to show the effectiveness of the proposed method.     

Stability and controllability of fuzzy singular dynamical systems

This paper is assigned to study the stability and controllability of fuzzy singular dynamical systems. Some new notions such as granular fuzzy matrix norm, the algebraic operations on the space of fuzzy matrices, fuzzy equilibrium point, and the granular fuzzy transfer function of fuzzy singular dynamical systems are introduced. Furthermore, by presenting some theorems proved in this paper, the fuzzy solutions of fully fuzzy singular dynamical systems are obtained. Moreover, some new notions regarding the analysis of the stability of fuzzy singular dynamical systems are given. The stability analysis underlies the concepts of fuzzy stable, fuzzy critical stable, and fuzzy unstable singular dynamical systems. Besides using the notions of controllability of the fuzzy slow and fast subsystems, the concept of granular controllability of the fuzzy singular dynamical system is investigated.     

企业技术管理体系构建研究

阐明了企业技术管理体系的概念,描述了技术管理体系的作用过程框架。在此基础上,分析了技术管理的三个维度和三个子系统,说明了各维度和子系统之间的作用关系。构建了企业技术管理体系的空间结构模型,从而为技术管理的研究奠定了新的基础。     

Sparse Bayesian learning approach for discrete signal reconstruction

This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome this challenge, we introduce a novel discretization enforcing prior to exploit the knowledge of the discrete nature of the signal-of-interest. By integrating the discretization enforcing prior into the SBL framework and applying the variational Bayesian inference (VBI) methodology, we devise an alternating optimization algorithm to jointly characterize the finite-alphabet feature and reconstruct the unknown signal. When the measurement matrix is i.i.d. Gaussian per component, we further embed the generalized approximate message passing (GAMP) into the VBI-based method, so as to directly adopt the ideal prior and significantly reduce the computational burden. Simulation results demonstrate substantial performance improvement of the two proposed methods over existing schemes. Moreover, the GAMP-based variant outperforms the VBI-based method with i.i.d. Gaussian measurement matrices but it fails to work for non i.i.d. Gaussian matrices.     

Analysis and tracer identification of multiphase transport systems with recycling

We consider the identification of the exchange coefficients of a multiphase spacially inhomogeneous transport system with absorbing-reflecting boundaries (i.e. recycling) by means of the tracer technique. A probabilistic approach is used. The model is described and equations are derived for functionals of the tracer distributions at the outputs. Previous results on identifiability and identification for the case without recycling are extended to the case of a recycling system.     

生态城市的系统框架构建研究——以广州市为例

在分析生态城市内涵的基础上构建生态城市的系统框架,包括复杂程度由高到低的3类系统和5个子系统,即属于人工抽象系统的战略系统、属于人类活动系统的社会生态系统和经济生态系统、属于人工物理系统的自然生态系统和人工设施系统;对5个子系统的基本内容及其相互关系进行阐述,并以广州市为例对广州生态城市建设的探索实践进行分析,为我国生态城市建设提供理论参考。     

A unifying and general account of fairness measurement in recommender systems

Fairness is fundamental to all information access systems, including recommender systems. However, the landscape of fairness definition and measurement is quite scattered with many competing definitions that are partial and often incompatible. There is much work focusing on specific – and different – notions of fairness and there exist dozens of metrics of fairness in the literature, many of them redundant and most of them incompatible. In contrast, to our knowledge, there is no formal framework that covers all possible variants of fairness and allows developers to choose the most appropriate variant depending on the particular scenario. In this paper, we aim to define a general, flexible, and parameterizable framework that covers a whole range of fairness evaluation possibilities. Instead of modeling the metrics based on an abstract definition of fairness, the distinctive feature of this study compared to the current state of the art is that we start from the metrics applied in the literature to obtain a unified model by generalization. The framework is grounded on a general work hypothesis: interpreting the space of users and items as a probabilistic sample space, two fundamental measures in information theory (Kullback–Leibler Divergence and Mutual Information) can capture the majority of possible scenarios for measuring fairness on recommender system outputs. In addition, earlier research on fairness in recommender systems could be viewed as single-sided, trying to optimize some form of equity across either user groups or provider/procurer groups, without considering the user/item space in conjunction, thereby overlooking/disregarding the interplay between user and item groups. Instead, our framework includes the notion of statistical independence between user and item groups. We finally validate our approach experimentally on both synthetic and real data according to a wide range of state-of-the-art recommendation algorithms and real-world data sets, showing that with our framework we can measure fairness in a general, uniform, and meaningful way.     

Topology identification and adaptive synchronization of uncertain complex networks with non-derivative and derivative coupling

This paper proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks with non-derivative and derivative coupling. By designing effective adaptive controller, the unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, numerical simulations are presented to verify the effectiveness of the theoretical results obtained in this paper.     

Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications

Probabilistic topic models are unsupervised generative models which model document content as a two-step generation process, that is, documents are observed as mixtures of latent concepts or topics, while topics are probability distributions over vocabulary words. Recently, a significant research effort has been invested into transferring the probabilistic topic modeling concept from monolingual to multilingual settings. Novel topic models have been designed to work with parallel and comparable texts. We define multilingual probabilistic topic modeling (MuPTM) and present the first full overview of the current research, methodology, advantages and limitations in MuPTM. As a representative example, we choose a natural extension of the omnipresent LDA model to multilingual settings called bilingual LDA (BiLDA). We provide a thorough overview of this representative multilingual model from its high-level modeling assumptions down to its mathematical foundations. We demonstrate how to use the data representation by means of output sets of (i) per-topic word distributions and (ii) per-document topic distributions coming from a multilingual probabilistic topic model in various real-life cross-lingual tasks involving different languages, without any external language pair dependent translation resource: (1) cross-lingual event-centered news clustering, (2) cross-lingual document classification, (3) cross-lingual semantic similarity, and (4) cross-lingual information retrieval. We also briefly review several other applications present in the relevant literature, and introduce and illustrate two related modeling concepts: topic smoothing and topic pruning. In summary, this article encompasses the current research in multilingual probabilistic topic modeling. By presenting a series of potential applications, we reveal the importance of the language-independent and language pair independent data representations by means of MuPTM. We provide clear directions for future research in the field by providing a systematic overview of how to link and transfer aspect knowledge across corpora written in different languages via the shared space of latent cross-lingual topics, that is, how to effectively employ learned per-topic word distributions and per-document topic distributions of any multilingual probabilistic topic model in various cross-lingual applications.     

Output reversibility in linear discrete-time dynamical systems

Output reversibility involves dynamical systems where for every initial condition and the corresponding output there exists another initial condition such that the output generated by this initial condition is a time-reversed image of the original output with the time running forward. Through a series of necessary and sufficient conditions, we characterize output reversibility in linear discrete-time dynamical systems in terms of the geometric symmetry of its eigenvalue set with respect to the unit circle in the complex plane. Furthermore, we establish that output reversibility of a linear continuous-time system implies output reversibility of its discretization. In addition, we present a control framework that allows to alter the system dynamics in such a way that a discrete-time system, otherwise not output reversible, can be made output reversible. Finally, we present numerical examples involving a discretization of a Hamiltonian system that exhibits output reversibility and an example of a controlled system that is rendered output reversible.     

Robust fault-tolerant control for four-wheel individually actuated electric vehicle considering driver steering characteristics

A robust fault-tolerant control scheme for distributed actuated electric vehicles is proposed to maintain vehicle stability suffering actuator faults while considering the driver personality differences. The proposed scheme integrates the cooperative game and terminal sliding mode control into the framework of the feedback linearization method (FLM). Firstly, the nonlinearities of the driver-vehicle system are treated by the knowledge of Lie derivative, and then a set of controllable virtual subsystems is obtained through diffeomorphism. To achieve multi-objective cooperation, the interaction framework of virtual subsystems is modeled based on cooperative game theory, which provides a basic feedback control scheme (BFCS). Finally, a terminal sliding mode technology-based active compensation control scheme is integrated into BFCS to handle the systemic disturbances caused by actuator faults. An implementation of hardware-in-the-loop verifies that the stability of the vehicle under the control of the developed approach can be guaranteed for different drivers and different fault types.     

H∞ filtering for nonlinear systems via neural networks

A novel H_∞ filter design methodology has been presented for a general class of nonlinear systems. Different from existing nonlinear filtering design, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions, which makes the structure of the desired filter simpler and parameter turning easier and has the advantages of guaranteed stability, numeral robustness, bounded estimation accuracy. A unified framework is established to solve the addressed H_∞ filtering problem by exploiting linear matrix inequality (LMI) approach. A numerical example shows that the filtering error systems will work well against bounded error between a nonlinear dynamical system and a multilayer neural network.