平行应急疏散系统:基本概念、体系框架及其应用

突发事件通常具有难以预测、多成因关联、危害性大及演变复杂等特点,应急情况下如何安全高效疏散人员是应急管控领域的重要研究内容.本文将基于人工系统（Artificial systems,A）、计算实验（Computational experiments,C）、平行执行（Parallel excution,P）方法的平行系统理论引入到应急管控领域,提出平行应急疏散系统（Parallel emergency evacuation systems,PeES）基本概念,构建系统体系框架及集成平台,并介绍人工应急疏散系统、计算实验、平行执行等主要功能模块的基本功能及实现方法.通过PeES能实现虚实应急疏散系统的管理与控制、应急方案的实验与评估以及相关人员的学习与培训.最后,以轨道交通枢纽站火灾场景下的乘客应急疏散为典型应用对平行应急疏散系统进行初步验证.     

仿真优化:理论与应用综述

对于现实中的复杂系统, 仿真优化是一种非常强大的分析和优化工具. 本文对仿真优化领域的相关理论与方法进行了介绍与回顾. 根据系统中决策变量的性质的不同(连续或者离散变量), 我们对仿真优化问题进行了分类. 而且我们对仿真优化领域中的重要技术进行了详细地讨论, 包括它们的原理、使用方法、优势和劣势以及应用等. 关于仿真优化领域未来的研究方向, 我们也进行了相关论述.     

视觉提示学习综述

近年来,随着提示学习方法在自然语言处理领域被提出,其日益受到研究人员广泛关注.它通过将各类下游任务重构成预训练任务的形式,以参数高效和数据高效的方式将大规模预训练模型应用在各类自然语言相关下游任务中.其中以GPT系列为代表的模型通过提示学习在对话生成和多模态图文理解等任务上取得了巨大的成功.然而,这类模型及方法还不能解决视觉中的稠密任务.受此启发,一些研究人员逐渐将提示学习广泛应用到视觉相关的各类任务当中,如图像识别、目标检测、图像分割、领域适应、持续学习等.由于目前还没有提示学习应用在视觉相关领域中的综述,本文将对视觉单模态领域以及视觉语言多模态领域的提示学习方法展开全面论述和分析.作为回顾,我们首先简要介绍自然语言处理领域的预训练模型,并对提示学习的基本概念、下游应用形式以及提示模板类型进行阐述和分类.其次,我们分别介绍视觉单模态领域以及视觉语言多模态领域里提示学习方法适配的预训练模型和任务.再次,我们分别介绍视觉单模态领域以及视觉语言多模态领域的提示学习方法.在自然语言处理领域,提示学习方法以继承预训练形式实现多任务统一为主要目的;与此不同,在视觉相关领域,提示学习方法侧重于面向...     

平行视觉:基于ACP的智能视觉计算方法

在视觉计算研究中,对复杂环境的适应能力通常决定了算法能否实际应用,已经成为该领域的研究焦点之一.由人工社会（Artificial societies）、计算实验（Computational experiments）、平行执行（Parallel execution）构成的ACP理论在复杂系统建模与调控中发挥着重要作用.本文将ACP理论引入智能视觉计算领域,提出平行视觉的基本框架与关键技术.平行视觉利用人工场景来模拟和表示复杂挑战的实际场景,通过计算实验进行各种视觉模型的训练与评估,最后借助平行执行来在线优化视觉系统,实现对复杂环境的智能感知与理解.这一虚实互动的视觉计算方法结合了计算机图形学、虚拟现实、机器学习、知识自动化等技术,是视觉系统走向应用的有效途径和自然选择.     

适用于大样本体系的实验设计方法

实验设计,尤其是混合水平实验设计,已经在科研领域和制造业中得到了广泛使用.虽然很多学者已经提出了许多实验设计理论以及实验设计用表,但是对于含有混合水平的大样本体系的实验设计方法仍然是相当困难的.本文根据Yong Guo提出的实验设计方法,并依据正交设计理论和遗传算法,把此方法改进为适用于大样本体系的实验设计方法.为了配合遗传算法,而改进了交叉与变异操作:为了从复杂体系的全实验设计中高效地获取某一次实验设计,引进了数学方法中的求余运算.最后,当把此实验设计方法应用于一个真实的、多因子多水平的体系时,获得了比较满意的实验方案.因此,对于大样本体系,此实验设计方法所得到的实验方案是可以被接受的.     

加强实验设计、引入分组教学,提高课堂实验的授课效果——记“.Net程序设计”课堂实验的设计

实验是程序设计课程中提高学生应用和实践能力的一个重要手段,本文针对.NET程序设计课程,讨论了实验设计中如实验内容、实验时间、实验流程以及实验的验收与评分标准等细节问题的设计思路;课堂实验设计的新颖之处在于将小组协作引入到实验中,并基于因材施教的思想,在实验中引入附加题。从课堂情况看,该实验设计的效果良好,实验设计的方案是可行的。     

网格资源模型综述

网格计算将广域网上的各种计算资源、信息资源、设备资源等集成起来,以统一的方式向用户提供服务,是当前网络计算领域的研究热点.其中资源模型是关系到广域分布式环境中资源共享和协同工作效率的关键.本文介绍了网格中的资源模型的主要研究内容,以及国内外相关领域的研究现状.     

浅谈智能计算及其研究方法

智能计算是最近兴起的一类仿生计算方法,已在许多实际领域中得到成功应用.系统地介绍几种有代表性的智能计算方法,其中包括人工神经网络、进化算法、蚁群算法以及粒子群算法.在此基础上,从仿生、实验算法学、群体智能、融合以及系统理论等角度对智能计算中的研究方法作适当探讨     

Abaqus在建筑结构领域的创新应用

Abaqus在建筑结构领域的应用主要集中在大震弹塑性时程分析上;另外,在钢结构复杂节点的设计和验算以及抗火计算等方面,Abaqus也是主要的计算软件之一.实际上Abaqus丰富多样的功能,可以在建筑结构设计方面找到更多的用途.将其静力和线性动力计算功能用于结构设计以及将其网格划分功能用于结构布置,是较为创新的应用,希望这些工作能够抛砖引玉,使Abaqus更好地服务于建筑结构的研究和设计工作.     

计算实验方法的溯源、现状与展望

随着信息技术的发展,复杂系统越来越多地呈现出社会、物理、信息相融合的特征.因为这些系统涉及到了人和社会的因素,其设计、分析、管理、控制和综合等问题正面临前所未有的挑战.在这种背景下,计算实验应运而生,通过“反事实”的算法化,为量化分析复杂系统提供了一种数字化和计算化方法.对于计算实验方法的发展现状与未来挑战进行了全面梳理:首先介绍了计算实验方法的概念起源与应用特征;然后详细阐述了计算实验的方法框架与关键步骤;接着展示了计算实验方法的典型应用,包括现象解释、趋势预测与策略优化;最后给出了计算实验方法所面临的一些关键问题与挑战.旨在梳理出计算实验方法的技术框架,为其快速发展与跨学科应用提供支撑.     

Utility and mechanism design in multi-agent systems: An overview

Future cities promise to be more autonomous than ever, largely owing to our ability of coordinating complex systems in real time: fleets of self-driving cars will offer on-demand transportation services, delivery drones will fly parcels in our skies, power plants will provide renewable energy reliably. In many of these systems, there is no single decision-maker with full information and authority. Instead, the system performance greatly depends on the decisions made by interacting entities with local information and limited communication capabilities. Game theory, intended as the study of multi-agent decision-making, is a fitting paradigm to tackle many of the associated challenges. Moving from this observation, in this paper we review how tools and ideas from game theory can be brought to bear on the coordination of multi-agent systems. At the heart of the proposed approach is the design and influence of agents’ preferences so that their local optimization induces a desirable system behavior. Its applicability spans a variety of settings irrespective of whether the decision makers are strategic (e.g., drivers in a road network), or not (e.g., delivery drones). Along the way, we also discuss future research directions and connections with related research areas including algorithmic game theory, incentive and mechanism design, economics, computational complexity, and approximation algorithms.     

Developing control-theoretic objectives for large-scale brain dynamics and cognitive enhancement

The development of technologies for brain stimulation provides a means for scientists and clinicians to directly actuate the brain and nervous system. Brain stimulation has shown intriguing potential in terms of modifying particular symptom clusters in patients and behavioral characteristics of subjects. The stage is thus set for optimization of these techniques and the pursuit of more nuanced stimulation objectives, including the modification of complex cognitive functions such as memory and attention. Control theory and engineering will play a key role in the development of these methods, guiding computational and algorithmic strategies for stimulation. In particular, realizing this goal will require new development of frameworks that allow for controlling not only brain activity, but also latent dynamics that underlie neural computation and information processing. In the current opinion, we review recent progress in brain stimulation and outline challenges and potential research pathways associated with exogenous control of cognitive function.     

Parallel vision for perception and understanding of complex scenes: methods,framework, and perspectives

In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.     

Learning from humans: computational modeling of face recognition

In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.     

城市客运交通枢纽平行系统体系研究

城市客运交通枢纽系统是一个典型的复杂巨系统, 传统的管理方法难以形成具有动态适应能力的有效解决方案. 运用复杂系统研究中的ACP (人工系统、计算机实验、平行执行)方法, 首次给出城市客运交通枢纽平行控制与管理系统研究框架. 以该框架为基础, 首先,分析了人工交通枢纽系统中模型的构成及支撑条件; 进而对人工交通枢纽系统计算实验中涉及的实验场景设计、实验内容设计进行了详细阐述; 最后,对人工交通枢纽系统平行执行内容进行了说明. 该体系框架的提出, 对于城市客运交通枢纽管控水平的提高具有重要指导意义.     

Reproducible research in computational economics: guidelines,integrated approaches,and open source software

Traditionally, computer and software applications have been used by economists to off-load otherwise complex or tedious tasks onto technology, freeing up time and intellect to address other, intellectually more rewarding, aspects of research. On the negative side, this increasing dependence on computers has resulted in research that has become increasingly difficult to replicate. In this paper, we propose some basic standards to improve the production and reporting of computational results in economics for the purpose of accuracy and reproducibility. In particular, we make recommendations on four aspects of the process: computational practice, published reporting, supporting documentation, and visualization. Also, we reflect on current developments in the practice of computing and visualization, such as integrated dynamic electronic documents, distributed computing systems, open source software, and their potential usefulness in making computational and empirical research in economics more easily reproducible.      

Models of parallel computation: a survey and classification

In this paper, the state-of-the-art parallel computational model research is reviewed. We will introduce various models that were developed during the past decades. According to their targeting architecture features, especially memory organization, we classify these parallel computational models into three generations. These models and their characteristics are discussed based on three generations classification. We believe that with the ever increasing speed gap between the CPU and memory systems, incorporating non-uniform memory hierarchy into computational models will become unavoidable. With the emergence of multi-core CPUs, the parallelism hierarchy of current computing platforms becomes more and more complicated. Describing this complicated parallelism hierarchy in future computational models becomes more and more important. A semi-automatic toolkit that can extract model parameters and their values on real computers can reduce the model analysis complexity, thus allowing more complicated models with more parameters to be adopted. Hierarchical memory and hierarchical parallelism will be two very important features that should be considered in future model design and research.     

Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field.