自主计算软件工程方法

从软件工程角度对自主计算进行研究，定义了一种自、主计算系统模型．以此模型为基础，提出了一种多主体系统设计方法DPMAS用于自主计算系统的建模，提出了一种基于构件复用的层次化自主计算未统开发方法．根据研究成果，实现了一个自主计算开发环境，较好的支撑了自主计算系统的软件工程过程．     

具有自主计算特征的新型网格体系结构研究

网格环境的复杂性和动态性迫切需要自主计算范型的支持。提出了一种具有自主计算特征的新型网格体系结构。网格的自我管理功能通过自主网格服务层、多Agent自主协同层和表达用户需求的政策层来完成。为了更好的实现Agent间的自主协同,提出了构造多Agent相似-关联树的算法。模拟环境下实现的网格原型系统的运行表明,符合该体系结构的网格系统具有一定程度的自配置、自优化、自保护和自愈合的能力。     

自主计算环境

自主计算技术是为了解决软件复杂性危机而提出的一门新兴的研究课题。该文分析了目前自主计算研究的现状和不足，提出了一种自主计算系统的建模方法，给出了自主单元和自主计算系统体系结构，并在该结构的基础上实现了自主计算环境ACE。     

基于层次式的实时智能计算体系结构研究

本文通过对RCS智能系统计算结构的改进，提出了一种具有更强鲁棒性的实时智能计算体系结构，该结构还具有紧急处理和学习能力。该计算结构在汽车智能自主驾驶系统中的应用，使得其性能得到了验证。     

具有自主计算特征的资源监控管理系统

日益增加的设备数量和繁多的设备类型是网络管理员需要面对的主要问题,它提高了对网络管理员的能力要求,同时又降低了管理的效率.而自主计算理念及技术是解决管理复杂性难题的热点.本文根据自主计算的特征和功能,设计实现了具有一定程度自识别、自配置、自组织、自运行、自稳定和自恢复功能的资源监控管理系统.     

自主系统中的一种优化模型设计

自优化是自主计算系统的一个基本能力，而性能函数为实现自主计算系统的这种能力提供了一个自然且基本的框架。自主元素是自主计算系统的基本建设模块，而被管理组件是自主元素的管理对象。文中将被管理组件划分为资源和服务，然后给出了性能函数的模型，从而提供了自优化的目标。最后，讨论了在自主元素环境下的自优化过程。     

基于移动计算的开放式教学系统的设计与实现

结合移动计算及流媒体技术在远程教学中的应用，设计一种基于移动计算的开放式教学系统，利用MPEG-4对视频信号进行编码，使用无线网络进行音频或视频信号传输，并基于Xscale架构的嵌入式系统自主开发设计了用户移动终端，最后实现了一个原型系统。系统具有良好的实时性、交互性和可扩展性。     

普适计算：人本计算

普适计算强调以人为本的计算思想，它主张计算应迎合人的习性，自主地与使用者产生互动，使用户能集中精力于所要完成的任务。在普适计算环境下，计算将融入到人们的日常生活之中，使人们能够随时随地无妨碍地获得计算和信息服务。综述了普适计算的发展历史、现状和研究热点，分析和讨论了普适计算的概念和特性，最后对目前一些具有代表性的普适计算研究项目进行了简要的介绍和分析。     

基于自律计算的系统可信性自调节模型

保障和增强系统的可靠性、安全性和稳定性等可信性质是对系统服务的高级要求。提出了一个基于自律计算理念的系统可信性自调节模型,该模型动态分析目标系统的可信度变化趋势,参考先验可信度变化曲线的梯度升降,依序实现可信度在线评估、可信度动态预测和自调节方案选择,完成系统可信度非降的自主调节过程,采用信赖域梯度计算策略解决自调节的最优化问题。     

自主计算概念模型与实现方法

自主计算是一个新兴的热点研究领域,旨在通过"技术管理技术"的手段隐藏系统管理复杂性,建立用户可指导的、状态觉察的和自适应的计算机系统.目前,自主计算的研究仍处于起步阶段,尚缺乏系统而成熟的理论体系.在阐明自主计算概念的基础上,提出一个自主计算概念模型.该模型刻画了自主元素和自主计算系统的基本工作机制和原理.以该模型为依据,概括性地提出了两类分别基于知识模型和数学模型的自主计算系统,分析它们的优点和不足.最后,给出了自主计算研究展望.     

一种基于SURF与地理格网模型的增强现实方法

图像识别与匹配是增强现实领域研究与应用的基础和关键,针对户外场景的广域性和随机性,以及目标纹理结构相似性等问题,提出一种基于SURF与地理格网模型的增强现实方法。该方法根据目标场景与地理位置的相关性,检测图像特征点并生成Location-SURF图像特征描述,基于地理格网模型构建空间四叉树索引,建成静态特征样本库。将视频帧、位置和角度信息生成特征图像,上传至服务端解析运算并与样本库训练匹配。选取宁波环球航运广场约0.376km2的区域,采集270余幅图像数据构建样本库并开展试验,通过现场图像的实时采集和计算,能够实现特征点的在线匹配,在此基础上通过调整点位距离比例的阈值,能够提升匹配的准确程度。基于该算法开发移动增强现实系统,运用四层技术架构实现了终端采集显示和服务端分析计算的并行化,达到真实场景与虚拟信息的融合显示效果。系统应用结果表明：该算法可以解决复杂环境下场景图像识别匹配率不高的问题,可快速地完成特征点的检测和提取,能够有效地进行样本训练和匹配,对户外移动增强现实进行了有益尝试并提供一种有效的途径。     

基于属性优先关系的多用户Skyline查询处理算法#br#

Skyline查询为多目标决策等问题提供了解决方案。每个用户对数据属性的优先关系的需求不同,传统算法无法有效解决多用户场景下的偏好Skyline查询。针对该问题,提出一种基于属性优先关系的多用户偏好Skyline查询算法--MUPS算法。该算法基于属性权重对原始Skyline结果集通过新的σ-支配方式进行剪枝;同时,通过交互动态修正属性的权重大小,使最终结果更符合用户群的真实偏好需求。最后,在模拟和真实数据上验证MUPS算法的有效性和良好的交互性能。     

Book Reviews

Books reviewed:
Maeda, John. Design by Numbers
Knuth, Donald E. Digital Typography
Farin, G., Bieri, H., Brunnet, G. and Derose, T. (Editors). Geometric Modelling
Knudson, Jonathan. Java 2D Graphics
Hege, H.-C. and Polthier, K. (Editors). Mathematical Visualization
Pickover, Clifford (Editor). Chaos and Fractals: A Computer Graphical Journey
Flake, Gary William. The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation
Strothotte, Thomas. Computational Visualization, Graphics, Abstraction and Interactivity
Russ, John C. The Image Processing Handbook (3rd edition)     

社会Agent的BDO模型

目前MAS中思维状态的研究趋势是在个体模型中加入社会思维属性,研究社会承诺、依赖、联合意图的推理关系。在BDI模型中,以意图为中心的观点不描述社会Agent。该文分析了以竭力为中心的Agent思维状态研究存在的问题,提出MAS的分层模型,并提出以信念、愿望和义务作为基本思维属性（简称BDO）来描述Agent的思维状态和社会属性,给出了BDO逻辑和语义模型,考虑了奖励、惩罚、承诺和承诺解除问题,并对队、组织、组织意图等现象给出了描述。该文力图更自然地描述社会性Agent的思维状态和群体概念,是对Rao和Georgeff提出的BDI模型的改进。最后通过一个例子说明了BDO的表达能力。进一步的工作包括建立更为完善的语义模型、结合各个思维属性的动态修正语义给出BDO Agent的动态模型以及给出基于BDO逻辑框架的Agent／MAS实现结构。     

Teaching mineral prospecting by computer-assisted simulation techniques

The simulation game, which involves the interaction of a geological model, a set of rules, and teaching materials, is used to teach students and professional people attending refresher courses the techniques of mineral exploration. Using geochemical and geophysical measurements, including detailed borehole logs, the students plan an exploration strategy, control the exploration budget, discover the structure and geology of the area represented by the model, outline probable mineralizations, and evaluate the ore reserves. They choose the borehole locations, the type of drilling machine, and the drilling depth, taking continually into consideration the financial aspects.The model is multidisciplinary, involving stratigraphy, sedimentology, structural geology, tectonics, climatic conditions, metallogeny, mineralogy, and petrology. Certain of these characteristics are expressed as complex mathematical functions, which are solved by the program. The computer program also prints out the detailed borehole logs, controls the budget and drilling equipment, and enforces the rules of the game.     

社交网络技术在科研教学环境中的应用研究

随着互联网产业的发展、用户规模的扩张和用户行为的变化,以Web2.0为特征的相关需求应运而生。SNS,Social Networking Services,专指旨在帮助人们建立社会性网络的互联网应用服务。依赖其开放性,即时性,互动性,不受时间地点约束等特点获得青睐,很多社交网站如twitter,Facebook,人人网,微博在流量、用户数和市值都有引人瞩目的成绩。社交网络的特性适用于知识的共享,将SNS和E-learning结合产生的面向知识管理的社交网络KMOS系统既使学生在社交网络服务中获取到知识,又打破传统教学受时间、地点的束缚,开放共享知识,一对多的高效传播模式。设计和实现的面向知识管理的社交网络服务拥有的上述优点。使用列表和群组控制多媒体资源的传播范围。分层和插件式设计使系统灵活应对需求改变。使用社交网络技术提高教学质量。     

The Algebraic Framework for Object-Oriented Systems

Category Theory is introduced as the mathematical model for object-oriented systems which are distributed, heterogeneous, real-time, embedded, and open-ended. Each object can be represented as an algebra. A collection of algebras with morphisms form a category if they satisfy some conditions. After a brief introduction of categorical concepts which are needed to formulate the framework for object-oriented systems, they are explicated in terms of objects. Then some system design methodologies such as SADT, JSD, MASCOT 3, OOD, HOOD, MOON, ADM 3, and Petri nets are examined in the categorical framework and classified into four groups: functional, process-based, object-oriented, and net-based. Combining theoretical and practical results, the interactive system design tool OBJ-NET is briefly introduced.     

Real-time pinch detection algorithm: Robust to impulsive noise

A robust pinch detection algorithm which can be implemented in a cheap microprocessor is proposed for the development of a safety feature in the automotive power window system. To solve the problems caused by the performance degradation of a Hall sensor or real driving situations, the proposed algorithm makes use of the H _∞ state estimation technique. The motivation of this approach comes from the advantage that the H _∞ filter can minimize or bound the worst-case estimation error energy for all bounded energy disturbances. Herein, the pinch torque rate estimator is derived from applying the steady-state H _∞ filter to the augmented model, which includes the motor dynamics and an additional torque rate state. Then, to redesign an appropriate estimator for real-time implementation, the torque rate estimate can be calculated more efficiently than the previous method [1]. Experimental results verify that, with a small amount of computation, the proposed pinch detection algorithm provides fast pinch detection performance superior to the existing method. Furthermore, it guarantees robustness against the worst-case measurement noises. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Young Il Lee. Jung-Hoon Park received the B.E. degree in Electronic Engineering in 1996, and the M.S. degree in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2002. He worked with Samsung Electronics as an Engineer from 1996 to 1999. He is currently pursuing his doctoral degree at Yonsei University. His research interests include robust control and filtering theory, robot vision, and its applications. Won-Sang Ra received the B.E., M.S., and Ph.D. degrees in Electrical and Electronics Engineering from Yonsei University, Seoul, Korea, in 1998, 2000, and 2009, respectively. From March 2000 to February 2009, he was with the Guidance and Control Department of Agency for Defense Development, Daejeon, as a Senior Researcher. Since March 2009, he has been with the School of Mechanical and Control Engineering, Handong Global University, where he is currently a Full-Time Instructor. His main research topic includes the robust filtering theory and its applications to autonomous vehicle guidance and control. Tae-Sung Yoon received the B.E., M.S., and Ph.D. degrees, in Electrical Engineering from Yonsei University, Seoul, Korea, in 1978, 1980, and 1988, respectively. He worked with the Department of Electrical Engineering at the 2nd Naval Academy, Jinhae, Korea, as a member of the teaching staff from 1980 to 1983. He worked with the Department of Electrical Engineering at Vanderbilt University, Nashville, as a Visiting Assistant Professor from 1994 to 1995. Since 1989, he has been with the Department of Electrical Engineering, Changwon National University, Changwon, Korea where he is currently a Professor. His research interests include robust filtering, mobile robotics, and time-frequency signal processing in instrumentation. Jin-Bae Park received the B.E. degree in Electrical Engineering from Yonsei University, Seoul, Korea, in 1977, and the M.S. and Ph.D. degrees in Electrical Engineering from Kansas State University, Manhattan, in 1985, and 1990, respectively. Since 1992, he has been with the Department of Electrical and Electronic Engineering, Yonsei University, where he is currently a Professor. His research interests include robust control and filtering, nonlinear control, mobile robotics, fuzzy logic control, neural networks, genetic algorithms, and Hadamard-transform spectroscopy. He has served as the Director for the Transactions of the Korean Institute of Electrical Engineers (1998–2003) and the Institute of Control, Automation, and Systems Engineers (1999–2003). He is currently the Editor-in-Chief for the International Journal of Control, Automation, and Systems.     

Toward a Meta-Model for Computational Engineering

Geometric modeling and finite element analysis have matured in recent decades. Both methods are used extensively in engineering design. However, the link between geometric modeling, physical modeling and finite element analysis is currently cumbersome, error-prone, and ad-hoc. Topological domain modeling provides the missing link. In this paper, we propose a combined topological modeling and finite element modeling method that allows not only topological modeling, but also promotes geometric and physical modeling, by providing a topological base space for the definition of finite element meshes, fields, and the definition and solution of boundary value problems. We call the method the Constructive Topological Domain Method (CTDM). In this method, Primitive Topological Domains (PTDs), each possessing a natural coordinate space, are combined in multiple n-dimensional Cartesian coordinate spaces, called charts, using generalizations of Boolean set operations, to create Constructed Topological Domains (CTDs) capable of acting as the base spaces of fiber bundles. The charts are glued together to create an atlas, within which the CTD is defined. The fiber of the bundle may describe, in addition to geometry, physical fields like density, stress, and temperature. Finite element meshes may be defined upon each of the PTDs from which the CTD is constructed, enabling the definition and solution of boundary value problems, thus avoiding the difficult and messy problem of creating a single finite element mesh to represent the entire CTD. A modified finite element method, to handle the individually meshed PTDs, is described. The boundary conditions may be specified as analytical or as finite element-based fields upon each of the PTDs. The CTDM appears to be a promising approach to robust mathematical and computational modeling of physical objects. Simple examples are presented. ID="A1"Correspondance and offprint requests to: W. Gerstle, Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA. E-mail: gerstle@unm.edu     

多特征融合的lncRNA识别与其功能预测

针对传统的基于单一特征的植物lncRNA识别的局限性,提出了融合RNA序列的开放阅读框、二级结构以及k-mers等多特征方法,训练高斯朴素贝叶斯、支持向量机和梯度提升决策树3种经典的分类模型,并实现分类结果的集成,利用交叉验证对模型的性能进行了评估,整体性能优于目前较流行的CPAT、CNCI和PLEK预测软件,在拟南芥数据集上总体的准确率达到了89%。另外,基于内源性竞争规则以及RNA结构信息,分别对lncRNA-microRNA和microRNA-mRNA进行靶向预测、筛选,再通过整合预测数据建立互作网络,并对网络模块中的lncRNA进行功能预测。通过GO术语分析,对与mRNA相关的lncRNA可能参与的生物调控过程进行预测,推测它们的相应功能。