一个多智能体机器人协作装配系统

介绍了一个基于多智能体概念实现的多机器人协作装配系统——ＭＲＣＡＳ（Ｍｕｌｔｉ－ＲｏｂｏｔＣｏｏｐｅｒａｔｉｖｅＡｓｓｍｂｌｙＳｙｓｔｅｍ）。该系统由组织级计算机、三台工业机器人和一台全方位移动小车（ＯＤＶ）组成，采用分层递阶体系结构。利用ＭＲＣＡＳ系统进行了多机器人协作装配的实验：在ＯＤＶ装配平台上，四台机器人合作装配一个大型桁架式工件。该工件具有多种装配构型，但任何一台机器人不能独立完成装配。

海拉尔盆地贝尔凹陷基岩潜山特征及储层预测

The lithology of the buried hill of Triassic Budate group in Beier depression is epimetamorphic clastic rock and volcanic clastic rock stratum. Recently the favorable hydrocarbon show was discovered in buried hill of base rock, and large-duty industrial oil stream was obtained in some wells in Beier depression. Based on the information of seismos and wells, the tectonic framework, tectonic deformation times and faulted system of the Beier depression are comprehensively studied, then configuration, evolutional history, genetic type and distributed regularity of buried hill are defined. According to observing description and analysis of core sample, well logging and interpretive result of FMI, the lithological component, diagenetic type and diagenetic sequence of buried hill reservoir are confirmed, then reservoir space system of buried hill is distinguished, and vegetal feature, genetic mechanism and distributed regularity of buried hill fissure are researched, at the same time the quantitative relationship is build up between core fissures and fissures interpreted by FMI. After that fundamental supervisory action of fault is defined to the vegetal degree of fissure, and the fissure beneficial places are forecasted using fractal theory and approach. At last the beneficial areas of Budate group reservoir are forecasted by reservoir appraisal parameters optimization such as multivariate gradually regression analysis et. al. and reservoir comprehensive appraisal method such as weighing analyze and clustering procedure et. al. which can provide foundation for the next exploratory disposition. Such production and knowledge are obtained in this text as those: 1. Four structural layers and two faulting systems are developed, and four structural layers are carved up by three bed succession boundary surfaces which creates three tectonic distortional times homology. Three types of buried hill are divided, they are ancient physiognomy buried hill, epigenetic buried hill, and contemporaneous buried hill. 2. Reservoir space of Budate buried hill is mainly secondary pore space and fissure, which distributes near the unconformity and/or inside buried hill in sections. The buried hill reservoir experienced multi-type and multi-stage diagenetic reconstruction, which led to the original porosity disappeared, and multi secondary porosity was created by dissolution, superficial clastation and cataclasis et. al. in diagenetic stage, which including middle crystal pore, inter crystal pore, moldic pore, inter particle emposieu, corrosion pore space and fissure et. al. which improved distinctly reservoir capability of buried hill. 3. The inner reservoir of buried hill in Beier depression is not stratigraphic bedded construction, but is fissure developing place formed by inner fault and broken lithogenetic belt. The fissures in inner reservoir of buried hill are developed unequally with many fissure types, which mainly are high angle fissure and dictyonal fissures and its developing degree and distribution is chiefly controlled by faulting. 4. The results of reservoir comprehensive evaluate and reservoir predicting indicates that advantageous areas of reservoir of buried hill chiefly distributes in Sudeerte, Beixi and Huoduomoer, which comprehensive evaluate mainly Ⅱand Ⅲ type reservoir. The clues and results of this text have directive significance for understanding the hydrocarbon reservoir condition of buried hill in Beier depression, for studying hydrocarbon accumulated mechanism and distributed regularity, and for guiding oil and gas exploration. The results of this text also can enrich and improve nonmarine hydrocarbon accumulated theory.

民众社会风险认知的影响因素及作用机制

Risk perception is one of important subjects in management psychology and cognitive psychology. It is of great value in the theory and practice to investigate the social risk events that the public cares a lot especially in this social transition period. Furthermore, this study explored the factors that influence the risk perception and the results caused by risk perception. A survey including 30 hazards and 8 risk attributes was designed and distributed to about 3, 200 residents of 8 districts, Beijing. The major findings are listed as following： Firstly, combining the methods of system science and psychology, GAE program was used to indentify 7 groups of social risk events, such as national safe, government management, social stability, general mood of society, economic and finance, resources and environment & daily life problems. This study provided substance for the following studies and it was also a new attempt in research method which is of certain reference value for the related researches. Secondly, a scale of societal risk perception was designed and 2 factors were identified (Dread Risk & Unknown Risk). Reliability analysis, EFA and CFA show the reliability and validity of the societal risk questionnaire is good enough. The investigation using this scale showed that older participants and higher socioeconomic status perceived the societal hazards to be more threatening than did younger participants and lower socioeconomic status. However, there is no gender difference. Thirdly, structural equation model was used to analyze the influence factors and mechanism of societal risk perception. Risk taking, government support and social justice could influence societal risk perception directly. Government support moderated the relationship between government trust and societal risk perception. Societal risk perception influenced life satisfaction, public policy preferences and social development belief. Multi-group analysis was used to find out that the participants who have different socioeconomic status express different mechanism. Fourthly, the result of the research was used to explore the risk event of 2008 Olympic game. The results showed that government support and preparation of Olympic game influenced societal risk perception directly. Preparation moderated the relationship between government trust and risk perception. Risk perception influenced worry, effect of Olympic game and belief of successl. This result proved that risk perception could be used as an indicator. The indictor of risk perception was used to identify the characteristics of higher risk perception group. Finally, suggestions to the related decision were provide to the government.

Design and Evaluation of the Hamal Parallel Computer

Parallel shared-memory machines with hundreds or thousands of processor-memory nodes have been built; in the future we will see machines with millions or even billions of nodes. Associated with such large systems is a new set of design challenges. Many problems must be addressed by an architecture in order for it to be successful; of these, we focus on three in particular. First, a scalable memory system is required. Second, the network messaging protocol must be fault-tolerant. Third, the overheads of thread creation, thread management and synchronization must be extremely low. This thesis presents the complete system design for Hamal, a shared-memory architecture which addresses these concerns and is directly scalable to one million nodes. Virtual memory and distributed objects are implemented in a manner that requires neither inter-node synchronization nor the storage of globally coherent translations at each node. We develop a lightweight fault-tolerant messaging protocol that guarantees message delivery and idempotence across a discarding network. A number of hardware mechanisms provide efficient support for massive multithreading and fine-grained synchronization. Experiments are conducted in simulation, using a trace-driven network simulator to investigate the messaging protocol and a cycle-accurate simulator to evaluate the Hamal architecture. We determine implementation parameters for the messaging protocol which optimize performance. A discarding network is easier to design and can be clocked at a higher rate, and we find that with this protocol its performance can approach that of a non-discarding network. Our simulations of Hamal demonstrate the effectiveness of its thread management and synchronization primitives. In particular, we find register-based synchronization to be an extremely efficient mechanism which can be used to implement a software barrier with a latency of only 523 cycles on a 512 node machine.

Interaction between coronal mass ejections and the solar wind

Jones, R. A.; Breen, A. R.; Fallows, R. A.; Canals, A.; Bisi, M. M.; Lawrence, G. (2007). Interaction between coronal mass ejections and the solar wind, Journal of Geophysical Research, 112, Issue A8 RAE2008

Communicable Memory and Lazy Barriers for Bulk Synchronous Parallelism in BSPk

Communication and synchronization stand as the dual bottlenecks in the performance of parallel systems, and especially those that attempt to alleviate the programming burden by incurring overhead in these two domains. We formulate the notions of communicable memory and lazy barriers to help achieve efficient communication and synchronization. These concepts are developed in the context of BSPk, a toolkit library for programming networks of workstations|and other distributed memory architectures in general|based on the Bulk Synchronous Parallel (BSP) model. BSPk emphasizes efficiency in communication by minimizing local memory-to-memory copying, and in barrier synchronization by not forcing a process to wait unless it needs remote data. Both the message passing (MP) and distributed shared memory (DSM) programming styles are supported in BSPk. MP helps processes efficiently exchange short-lived unnamed data values, when the identity of either the sender or receiver is known to the other party. By contrast, DSM supports communication between processes that may be mutually anonymous, so long as they can agree on variable names in which to store shared temporary or long-lived data.

User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution.

Monitoring behaviours and experiences before, during and after pregnancy in Ireland

Background: On-going surveillance of behaviours during pregnancy is an important but overlooked population health activity that is particularly lacking in Ireland. Few, if any, nationally representative estimates of most maternal behaviours and experiences are available. While on-going surveillance of maternal behaviours has not been a priority thus far in European countries including Ireland, on-going surveillance was identified as a key priority in the United States (US) during the 1980’s when the Pregnancy Risk Assessment Monitoring System (PRAMS), was established. Today, PRAMS is the only surveillance programme of maternal behaviours and experiences world-wide. Although on-going prevalence estimates are required in Ireland, studies which examine the offspring health effects of maternal behaviours are also required, since various questions regarding maternal exposures and their offspring health effects remain unanswered. Gestational alcohol consumption is one such important maternal exposure which is common in pregnancy, though its offspring health effects are unclear, particularly at lower or moderate levels. Thus, guidelines internationally have not reached consensus on safe alcohol recommendations for pregnant women. The aims of this thesis are to implement the PRAMS in Ireland (PRAMS Ireland), to describe the prevalence of health behaviours around the time of pregnancy in Ireland and to examine the effect of health behaviours on pregnancy and child outcomes (specifically the relationship between alcohol use during pregnancy and infant and child growth). Structure: In Chapter 1, a brief background and rationale for the work, as well as the thesis aims and objective is provided. A detailed description of the design and implementation of PRAMS Ireland is described in Chapter 2. Chapter 3 and Chapter 4 describe the methodological results of the implementation of the PRAMS Ireland pilot study and PRAMS Ireland main study. In Chapter 5, a comparison of alcohol prevalence in two Irish studies (PRAMS Ireland and Growing up in Ireland (GUI)) and one multi-centre prospective cohort study, Screening for Pregnancy Endpoints (SCOPE) Study is detailed. Chapter 6 describes findings on adherence to National Clinical Guidelines on health behaviours and nutrition around the time of pregnancy in PRAMS Ireland. Findings on exposure to alcohol use in pregnancy and infant growth outcomes are described in Chapter 7 and Chapter 8. The results of analysis conducted to examine the impact of gestational alcohol use on offspring growth trajectories to age ten are described in Chapter 9. Finally, a discussion of the findings, strengths and limitations of the thesis, direction for future research, policy, practice and public health implications are discussed in Chapter 10.Results: Implementation of PRAMS: PRAMS may be an effective system for the surveillance of health behaviours around the time of pregnancy in the Irish context. PRAMS Ireland had high response rates (67% and 61% response rates in the pilot and main study respectively), high item completion rates and valid prevalence estimates for many health behaviours. Examining prevalence of health behaviours: We found high levels of alcohol consumption before and during pregnancy, poor adherence to healthy diets and high levels of smoking before and during pregnancy among women in Ireland. Socially disadvantaged women had higher rates of deleterious health behaviours before pregnancy, although women with the most deleterious behaviour profiles before pregnancy appeared to experience the greatest gain in protective health behaviours during pregnancy. The impact of alcohol use on infant and offspring growth: We found that low and moderate levels of alcohol use did not impact on birth outcomes or offspring growth whereas heavy alcohol consumption resulted in reduced birth length and birth weight; however, this finding was not consistently observed across all studies. Selection, reporting and confounding biases which are common in observational research could be masking harmful effects. Conclusion: PRAMS is a valid and feasible method of surveillance of health behaviours around the time of pregnancy in Ireland. A surveillance program of maternal behaviours and experiences is immediately warranted due to high levels of deleterious health behaviours around the time of pregnancy in Ireland. Although our results do not indicate any evidence of harm, given the quality of evidence available, abstinence and advice of abstinence from alcohol may be the most prudent choice for patients and healthcare professionals respectively. Further studies of the effects of gestational alcohol use are required; particularly those which can reduce selection bias, reporting bias and confounding.

The Multiscale Systems Immunology project: software for cell-based immunological simulation.

BACKGROUND: Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing. RESULTS: The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales. CONCLUSION: MSI addresses the need for a flexible and high-performing agent based model of the immune system.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-researched topic within Artificial Intelligence (AI). We argue that the new model of belief revision as discussed here is suitable for general modelling of judicial decision making, along with the extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interacting with, and influencing, other agents who are deliberating collectively. The principle of 'priority to the incoming information', as known from AI models of belief revision, is problematic when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet, we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stimuli) could attempt to handle other aspects of the deliberation which are more specific to legal narratives, to argumentation in court, and then to the debate among the jurors.

Modelling technologies and applications

Numerical modelling technology and software is now being used to underwrite the design of many microelectronic and microsystems components. The demands for greater capability of these analysis tools are increasing dramatically, as the user community is faced with the challenge of producing reliable products in ever shorter lead times. This leads to the requirement for analysis tools to represent the interactions amongst the distinct phenomena and physics at multiple length and timescales. Multi-physics and Multi-scale technology is now becoming a reality with many code vendors. This chapter discusses the current status of modelling tools that assess the impact of nano-technology on the fabrication/packaging and testing of microsystems. The chapter is broken down into three sections: Modelling Technologies, Modelling Application to Fabrication, and Modelling Application to Assembly/Packing and Modelling Applied for Test and Metrology.

Environmental context explains Lévy and Brownian movement patterns of marine predators

An optimal search theory, the so-called Levy-flight foraging hypothesis(1), predicts that predators should adopt search strategies known as Levy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey(2-4). Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Levy behaviour has recently been questioned(5,6). Consequently, whether foragers exhibit Levy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory's central predictions. Here we use maximum-likelihood methods to test for Levy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Levy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Levy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Levy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Levy-flight foraging hypothesis(1,7), supporting the contention(8,9) that organism search strategies naturally evolved in such a way that they exploit optimal Levy patterns.

A comparison between ultraviolet disinfection and copper alginate beads within a vortex bioreactor for the deactivation of bacteria in simulated waste streams with high levels of colour, humic acid and suspended solids

We show in this study that the combination of a swirl flow reactor and an antimicrobial agent (in this case copper alginate beads) is a promising technique for the remediation of contaminated water in waste streams recalcitrant to UV-C treatment. This is demonstrated by comparing the viability of both common and UV-C resistant organisms in operating conditions where UV-C proves ineffective - notably high levels of solids and compounds which deflect UV-C. The swirl flow reactor is easy to construct from commonly available plumbing parts and may prove a versatile and powerful tool in waste water treatment in developing countries.

La Web Semántica y los archivos de televisión: estado de la cuestión

Se analizan y describen las principales líneas de trabajo de la Web Semántica en el ámbito de los archivos de televisión. Para ello, se analiza y contextualiza la web semántica desde una perspectiva general para posteriormente analizar las principales iniciativas que trabajan con lo audiovisual: Proyecto MuNCH, Proyecto S5T, Semantic Television y VideoActive.