科学和工程计算的新方法——无网格方法

当今科学活动可分为理论、实验和计算3种.随着计算科学和技术的迅速发展,计算将在科学研究和工程分析中发挥越来越重要的作用.特别是目前很多特大工程如原子弹、导弹、大型舰船和飞机的设计制造等,除了少量实验之外几乎完全依赖于计算和分析.     

面向科学与工程计算的网上实验室设计与开发

科学与工程计算问题是当代信息技术时代最富挑战性的科学领域之一。研究和开发了面向科学与工程计算的网上实验室系统,它由实验演示、实时指导、协同工作、讨论交流、资料室管理等子系统组成。该网上实验室不仅能促进专业人员对科学与工程计算的研究,而且能提高学生对学习数值计算课程的兴趣和效率。     

Editorial: Java for computational science and engineering – simulation and modeling

We are pleased to present a set of papers discussing the role of Java in Science and Engineering Simulation. These were presented at a small workshop with 45 participants at Syracuse on 16-17 December 1996. This was very successful, and a follow-up event will be sponsored by ACM in Las Vegas on 21 June 1997. The growing interest in this field is also supported by an email discussion list and other materials collected at the web site http://www.npac.syr.edu/projects/javaforcse. Java and Web technology can be used in many areas of science and engineering computation. These include sophisticated user interfaces and coarse-grain integration of different modules in complex meta-applications. However, also interesting (and controversial) is perhaps the use of Java as the language used for the computationally intense parts of a scientific code. All these areas were discussed at the workshop, with promising initial results and studies reported in each case. Again applications were described both for large-scale event-driven and time-stepped simulations and also for smaller client-side applets aimed at education. The appeal of Java as a simulation language includes its object-oriented characteristics, elegant applet software distribution model and natural support of graphical user interfaces. There are also non-technical reasons to think Java will be very important. In particular, one expects children to learn Java naturally as part of their Web experiences. On entering University, I find it hard to believe that many will be willing to switch from Java to Fortran77 or Fortran90. The papers in this issue fall into five areas. The first paper (‘Java for parallel computing and as a general language for scientific and engineering simulation and modeling’ by Geoffrey C. Fox and Wojtek Furmanski) is a general overview and the next three (‘Optimizing Java bytecodes’ by Michał Cierniak an Wei Li; ‘Optimizing Java: theory and practice’ by Zoran Budimlic and Ken Kennedy; ‘Technologies for ubiquitous supercomputing: a Java interface to the Nexus communication system’ by Ian Foster, George K. Thiruvathukal and Steven Tuecke) describe base Java technology from optimized compilation to linkage with communication infrastructure. The next two papers (‘Java simulations for physics education’ by Simeon Warner, Simon Catterall and Edward Lipson; ‘Using Java and JavaScript in the Virtual Programming Laboratory: a Web-based parallel programming environment’ by Kivanc Dincer and Geoffrey C. Fox) describe uses of Java in both science and computer science education. Then we have two papers (‘Java's role in distributed collaboration by Marina Chen and James Cowie’; ‘Java enabling collaborative education, health care, and computing’ by Lukasz Beca, Gang Cheng, Geoffrey C. Fox, Tomasz Jurga, Konrad Olszewski, Marek Podgorny, Piotr Sokolowski and Krzysztof Walczak) centred on the fascinating field of collaboration. The last six papers study the critical area of parallel and distributed computing in Java. These discuss world-wide computing (‘SuperWeb: research issues in Java-based global computing’ by Albert D. Alexandrov, Maximilian Ibel, Klaus E. Schauser and Chris J. Scheiman), large-scale software integration with Java servers (‘WebFlow – a visual programming paradigm for Web/Java based coarse grain distributed computing’ by Dimple Bhatia, Vanco Burzevski, Maja Camuseva, Geoffrey Fox, Wojtek Furmanski and Girish Premchandran) and mobility (‘Resource-aware metacomputing’ by Anurag Acharya, M. Ranganathan and Joel Saltz). These three distributed computing studies are contrasted with three on parallel computing: ‘Automatically exploiting implicit parallelism in Java’ by Aart J. C. Bik and Dennis B. Gannon on shared memory; ‘SPMD programming in Java’ by Susan Flynn Hummel, Ton Ngo and Harini Srinivasan on the SPMD style, and ‘Experiments with “HP Java”’ by Bryan Carpenter, Yuh-Jye Chang, Geoffrey Fox, Donald Leskiw and Xiaoming Li on classic distributed memory data parallelism. Currently, it appears that Java promises the computational scientist programming environments which have both attractive user interfaces and high-performance execution. An important purpose of the first workshop and the follow-up events is to get a broad input and study of the issues in this field so that we can guide the rapidly moving Java juggernaut to be maximally effective for scientific and engineering computation. © 1997 John Wiley & Sons, Ltd.     

rfid in pervasive computing: State-of-the-art and outlook

rfid has already found its way into a variety of large scale applications and arguably it is already one of the most successful technologies in the history of computing. Beyond doubt, rfid is an effective automatic identification technology for a variety of objects including natural, manufactured and handmade artifacts; humans and other species; locations; and increasingly media content and mobile services. In this survey we consider developments towards establishing rfid as the cost-effective technical solution for the development of open, shared, universal pervasive computing infrastructures and look ahead to its future. In particular, we discuss the ingredients of current large scale applications; the role of network services to provide complete systems; privacy and security implications; and how rfid is helping prototype emerging pervasive computing applications. We conclude by identifying common trends in the new applications of rfid and ask questions related to sustainable universal deployment of this technology.     

Distributed computing practice for large‐scale science and engineering applications

It is generally accepted that the ability to develop large‐scale distributed applications has lagged seriously behind other developments in cyberinfrastructure. In this paper, we provide insight into how such applications have been developed and an understanding of why developing applications for distributed infrastructure is hard. Our approach is unique in the sense that it is centered around half a dozen existing scientific applications; we posit that these scientific applications are representative of the characteristics, requirements, as well as the challenges of the bulk of current distributed applications on production cyberinfrastructure (such as the US TeraGrid). We provide a novel and comprehensive analysis of such distributed scientific applications. Specifically, we survey existing models and methods for large‐scale distributed applications and identify commonalities, recurring structures, patterns and abstractions. We find that there are many ad hoc solutions employed to develop and execute distributed applications, which result in a lack of generality and the inability of distributed applications to be extensible and independent of infrastructure details. In our analysis, we introduce the notion of application vectors: a novel way of understanding the structure of distributed applications. Important contributions of this paper include identifying patterns that are derived from a wide range of real distributed applications, as well as an integrated approach to analyzing applications, programming systems and patterns, resulting in the ability to provide a critical assessment of the current practice of developing, deploying and executing distributed applications. Gaps and omissions in the state of the art are identified, and directions for future research are outlined. Copyright © 2012 John Wiley & Sons, Ltd.     

Cloud-DLS: Dynamic trusted scheduling for Cloud computing

Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way.     

Cloud computing burst system (CCBS): for exa-scale computing system

Computational scientific applications tend to be very data I/O intensive, producing a large amount of data as the execution result. In this research, we propose a new storage system using next-generation non-volatile memory that is suitable for exa-scale computing systems. This storage system is called the Cloud Computing Burst System (CCBS) and is composed of a unified table management module, data scoring module, and CCBS storage. In particular, CCBS operates as a workload enlightened storage system using its own data scoring module. The CCBS storage architecture consists of PCM/NAND Flash arrays and a data migration engine. CCBS storage cannot only provide a scaling out feature, but also improve the overall performance of the storage system. In addition, by using new non-volatile memory array, many benefits, such as low energy consumption, density scaling, and high performance, can be achieved. We demonstrate the effectiveness of our proposed system by simulating the storage system using scientific benchmarking tool. Our data scoring algorithm can provide 7% more hit rate than other methods for CCBS. In addition, our proposed system has improved storage system speed by 1.64 times, compared with only NAND Flash conventional model.     

Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook

The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.     

云计算在安防视频监控领域的应用研究

大数据来临的时代下,安防视频监控系统与云计算技术的结合是未来的发展趋势,云计算的优势在于将大量的数据采取分布式的存储、计算、检索来大大提升安防视频监控的数据运用的效率,本文从云计算在安防视频监控中使用的必要性,社会发展需求和云计算当前平台的运用进行了简要的综合和介绍。为安防系统云计算的推广和深入发展提供一些借鉴。     

Basic research in computer science and software engineering at SKLCS

The State Key Laboratory of Computer Science (SKLCS) is committed to basic research in computer science and software engineering. The research topics of the laboratory include: concurrency theory, theory and algorithms for real-time systems, formal specifications based on context-free grammars, semantics of programming languages, model checking, automated reasoning, logic programming, software testing, software process improvement, middleware technology, parallel algorithms and parallel software, computer graphics and human-computer interaction. This paper describes these topics in some detail and summarizes some results obtained in recent years.     

Cloud computing in cryptography and steganography

We consider the new statements of problems in cryptography and steganography, which depend on the features of cloud systems. We also analyze the aspects of developing and implementing crypto- and steganosystems for cloud computing.     

Basic research in computer science and software engineering at SKLCS

The State Key Laboratory of Computer Science (SKLCS) is committed to basic research in computer science and software engineering. The research topics of the laboratory include: concurrency theory, theory and algorithms for real-time systems, formal specifications based on context-free grammars, semantics of programming languages, model checking, automated reasoning, logic programming, software testing, software process improvement, middleware technology, parallel algorithms and parallel software, computer graphics and human-computer interaction. This paper describes these topics in some detail and summarizes some results obtained in recent years.     

多示例学习及其研究现状

较全面地介绍和分析了第4种机器学习框架的多示例学习(MIL)．首先通过数学表达式对多示例学习进行描述，概括了其主要性质；然后总结了目前主要的求解多示例学习问题的算法，剖析了这些算法的主要思想；最后对多示例学习的未来发展作了展望．     

光学电流传感器及其研究现状

光学电流传感器（OCS）相比于传统电流互感器（CT）有着突出的优点,经过长时间的发展,已开始逐步取代传统CT,应用前景十分广阔.根据测量原理的不同,对OCS进行了分类,并对每种类型OCS的测量原理进行了简要介绍,着重对这几种类型的OCS在现阶段研究过程中所存在的关键问题和相应的解决方法进行了阐述.最后,对这几种类型OCS下一步的研究重点分别进行了说明.