一个用于化工企业的计算机集成基础结构

简介了国家“八五”攻关项目中大型过程工业综合自动化系统集成基础结构（集成器）的结构，各分系统的信息交换，以及集成器中的各服务器，该计算机集成基础结构在永新—沈阳化工股份有限公司运行。

一个先进的工业机器人计算机控制系统——ARCS

随着工业机器人及其应用的不断发展,要求一个强有力的计算机系统来控制它的工作,并具有灵活、方便的机器人编程语言.本文系统地介绍了我们自行设计并实现的一个先进的机器人控制系统——ARCS.该系统主要包括两部分:(1)一个实时多任务的机器人控制软件SVAL系统,该系统支持一种通用性较强的机器人编程语言——SVAL语言.(2)一个支持该软件系统工作的、具有开放式结构的硬件环境.ARCS系统具有良好的实时性、可扩展性及基于外部传感器信号进行控制的能力.由于该系统的开放式结构.使其根据不同要求可方便地增删其功能,并可控制不同类型的机器人.我们已成功地实现对PUMA760机器人的控制,并在其上引入了力觉与接近觉的传感器,采样时间可缩短到16ms.一年多的运行结果证明,该系统稳定可靠,性能良好,现在正向产品转化.

地震力对高架桥梁上轨道车辆的动态分析与安全性研究

The stability and derailment behavior analysis of railway vehicle system has been discussed by many papers in the past. In stability, give first place to consider hunting behavior of vehicle, therefore most of papers was only consider lateral and yaw motion, but vertical motion is the important factor in derailment behavior, and it will be quite effect in stability. We will probe the running stability and derailment behavior of railway vehicle moving on the viaduct in this paper. In this paper, we use Nadal’s formula to get the derailment quotient. In this paper, the railway vehicle is considered to be three subsystems, carbody, bogie and wheelset. There are secondary suspension systems between carbody and bogies, and primary suspension systems connecting bogies and wheelsets. A vehicle with vertical, lateral, roll, and yaw directions motion is considered to derive the mathematical equations. A vehicle with three-dimensional model has 16 degrees of freedom is used to develop the equations of train motion. In this study, results show that the track shift force and derailment factor increase with an increase of ground acceleration. But for the track shift force and derailment factor, the effects of track irregularities and train speed are very small. Key words: earthquake, railway vehicle, viaduct, derailment factor.

空间及言语工作记忆任务的情绪效应：来自ERP/ fMRI的证据

In the present study, based on processing efficiency theory, we used the event-related potentials (ERP) and functional magnetic resonance image (fMRI) techniques to explore the underlying neutral mechanism of influences of negative emotion on three subsystems of working memory, phonological loop、 visuospatial sketh pad and the central executive. The modified DSMT (delayed matching-to-sample task) and n-back tasks were adopted and IAPS (International Affective Picture System) pictures were employed to induce the expected emotional state of subjects. The main results and conclusions obtained in the series of experiments are as the following: 1. In DSM tasks, we found P200 and P300 were reduced by negative emotion in both spatial and verbal tasks, however the increased negative slow wave were only observed in spatial tasks, not in verbal tasks. 2. In n-back tasks, the updating function of WM associated P300 was affected by negative emotion only in spatial tasks, not in verbal tasks. Current density analysis revealed strong current density in the fronto-parietal cortex only in the spatial tasks as well. 3. We adopted fMRI-block design and ROIs analysis, and found significant emotion and task effects in spatial WM-associated right superior parietal cortex; only emotion effect in verbal WM-associated Broca’s area; the interaction effect in attention-associated medial prefrontal area and bilateral inferior parietal cortex. These results implied the negative emotion mainly disturbed the spatial WM-related areas, and the attention control system play a key role in the interaction of spatial WM and negative emotion. 4. to further examine the effects of positive、negative and neutral emotion on tasks with different cognitive loads, the selective effect of emotion on the ERP components of spatial WM was only found in 2-back tasks, not in visual searching tasks. So, firstly the positive emotion as well as negative emotion selectively disturbed on spatial WM in light of the attention resource competition mechanism. Secondly, the selective influences based on the different WM systems, not the properties of spatial and verbal information. At last, the manner of the interaction of emotion and cognition is correlated with the cognitive load.

Battling Reality

In the four years that the MIT Mobile Robot Project has benn in existence, we have built ten robots that focus research in various areas concerned with building intelligent systems. Towards this end, we have embarked on trying to build useful autonomous creatures that live and work in the real world. Many of the preconceived notions entertained before we started building our robots turned out to be misguided. Some issues we thought would be hard have worked successfully from day one and subsystems we imagined to be trivial have become tremendous time sinks. Oddly enough, one of our biggest failures has led to some of our favorite successes. This paper describes the changing paths our research has taken due to the lessons learned from the practical realities of building robots.

A Theory of How the Brain Might Work

I wish to propose a quite speculative new version of the grandmother cell theory to explain how the brain, or parts of it, may work. In particular, I discuss how the visual system may learn to recognize 3D objects. The model would apply directly to the cortical cells involved in visual face recognition. I will also outline the relation of our theory to existing models of the cerebellum and of motor control. Specific biophysical mechanisms can be readily suggested as part of a basic type of neural circuitry that can learn to approximate multidimensional input-output mappings from sets of examples and that is expected to be replicated in different regions of the brain and across modalities. The main points of the theory are: -the brain uses modules for multivariate function approximation as basic components of several of its information processing subsystems. -these modules are realized as HyperBF networks (Poggio and Girosi, 1990a,b). -HyperBF networks can be implemented in terms of biologically plausible mechanisms and circuitry. The theory predicts a specific type of population coding that represents an extension of schemes such as look-up tables. I will conclude with some speculations about the trade-off between memory and computation and the evolution of intelligence.

Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs

C.J.Price, D.R.Pugh, N.A.Snooke, J.E.Hunt, M.S.Wilson, Combining Functional and Structural Reasoning for Safety Analysis of Electrical Designs, Knowledge Engineering Review, vol 12:3, pp.271-287, 1997.

Nowe metody wyznaczania parametru asymetrii tensora gradientu pola elektrycznego w spektroskopii NQR i ich zastosowanie do badania układów nieuporządkowanych

Wydział Fizyki

Safe Compositional Network Sketches: Tool & Use Cases

NetSketch is a tool that enables the specification of network-flow applications and the certification of desirable safety properties imposed thereon. NetSketch is conceived to assist system integrators in two types of activities: modeling and design. As a modeling tool, it enables the abstraction of an existing system so as to retain sufficient enough details to enable future analysis of safety properties. As a design tool, NetSketch enables the exploration of alternative safe designs as well as the identification of minimal requirements for outsourced subsystems. NetSketch embodies a lightweight formal verification philosophy, whereby the power (but not the heavy machinery) of a rigorous formalism is made accessible to users via a friendly interface. NetSketch does so by exposing tradeoffs between exactness of analysis and scalability, and by combining traditional whole-system analysis with a more flexible compositional analysis approach based on a strongly-typed, Domain-Specific Language (DSL) to specify network configurations at various levels of sketchiness along with invariants that need to be enforced thereupon. In this paper, we overview NetSketch, highlight its salient features, and illustrate how it could be used in applications, including the management/shaping of traffic flows in a vehicular network (as a proxy for CPS applications) and in a streaming media network (as a proxy for Internet applications). In a companion paper, we define the formal system underlying the operation of NetSketch, in particular the DSL behind NetSketch's user-interface when used in "sketch mode", and prove its soundness relative to appropriately-defined notions of validity.

Safe Compositional Network Sketches: The Formal Framework

NetSketch is a tool for the specification of constrained-flow applications and the certification of desirable safety properties imposed thereon. NetSketch is conceived to assist system integrators in two types of activities: modeling and design. As a modeling tool, it enables the abstraction of an existing system while retaining sufficient information about it to carry out future analysis of safety properties. As a design tool, NetSketch enables the exploration of alternative safe designs as well as the identification of minimal requirements for outsourced subsystems. NetSketch embodies a lightweight formal verification philosophy, whereby the power (but not the heavy machinery) of a rigorous formalism is made accessible to users via a friendly interface. NetSketch does so by exposing tradeoffs between exactness of analysis and scalability, and by combining traditional whole-system analysis with a more flexible compositional analysis. The compositional analysis is based on a strongly-typed Domain-Specific Language (DSL) for describing and reasoning about constrained-flow networks at various levels of sketchiness along with invariants that need to be enforced thereupon. In this paper, we define the formal system underlying the operation of NetSketch, in particular the DSL behind NetSketch's user-interface when used in "sketch mode", and prove its soundness relative to appropriately-defined notions of validity. In a companion paper [6], we overview NetSketch, highlight its salient features, and illustrate how it could be used in two applications: the management/shaping of traffic flows in a vehicular network (as a proxy for CPS applications) and in a streaming media network (as a proxy for Internet applications).

Distributed Packet Rewriting and its Application to Scalable Server Architectures

To construct high performance Web servers, system builders are increasingly turning to distributed designs. An important challenge that arises in distributed Web servers is the need to direct incoming connections to individual hosts. Previous methods for connection routing have employed a centralized node which handles all incoming requests. In contrast, we propose a distributed approach, called Distributed Packet Rewriting (DPR), in which all hosts of the distributed system participate in connection routing. We argue that this approach promises better scalability and fault-tolerance than the centralized approach. We describe our implementation of four variants of DPR and compare their performance. We show that DPR provides performance comparable to centralized alternatives, measured in terms of throughput and delay under the SPECweb96 benchmark. Finally, we argue that DPR is particularly attractive both for small scale systems and for systems following the emerging trend toward increasingly intelligent I/O subsystems.

Type Systems for a Network Specification Language With Multiple-Choice Let

When analysing the behavior of complex networked systems, it is often the case that some components within that network are only known to the extent that they belong to one of a set of possible "implementations" – e.g., versions of a specific protocol, class of schedulers, etc. In this report we augment the specification language considered in BUCSTR-2004-021, BUCS-TR-2005-014, BUCS-TR-2005-015, and BUCS-TR-2005-033, to include a non-deterministic multiple-choice let-binding, which allows us to consider compositions of networking subsystems that allow for looser component specifications.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Cortical Dynamics of Visual Motion Perception: Short-Range and Long Range Apparent Motion

This article describes further evidence for a new neural network theory of biological motion perception that is called a Motion Boundary Contour System. This theory clarifies why parallel streams Vl-> V2 and Vl-> MT exist for static form and motion form processing among the areas Vl, V2, and MT of visual cortex. The Motion Boundary Contour System consists of several parallel copies, such that each copy is activated by a different range of receptive field sizes. Each copy is further subdivided into two hierarchically organized subsystems: a Motion Oriented Contrast Filter, or MOC Filter, for preprocessing moving images; and a Cooperative-Competitive Feedback Loop, or CC Loop, for generating emergent boundary segmentations of the filtered signals. The present article uses the MOC Filter to explain a variety of classical and recent data about short-range and long-range apparent motion percepts that have not yet been explained by alternative models. These data include split motion; reverse-contrast gamma motion; delta motion; visual inertia; group motion in response to a reverse-contrast Ternus display at short interstimulus intervals; speed-up of motion velocity as interfiash distance increases or flash duration decreases; dependence of the transition from element motion to group motion on stimulus duration and size; various classical dependencies between flash duration, spatial separation, interstimulus interval, and motion threshold known as Korte's Laws; and dependence of motion strength on stimulus orientation and spatial frequency. These results supplement earlier explanations by the model of apparent motion data that other models have not explained; a recent proposed solution of the global aperture problem, including explanations of motion capture and induced motion; an explanation of how parallel cortical systems for static form perception and motion form perception may develop, including a demonstration that these parallel systems are variations on a common cortical design; an explanation of why the geometries of static form and motion form differ, in particular why opposite orientations differ by 90°, whereas opposite directions differ by 180°, and why a cortical stream Vl -> V2 -> MT is needed; and a summary of how the main properties of other motion perception models can be assimilated into different parts of the Motion Boundary Contour System design.