Embeddable and locatable spinning

A novel spinning method: embeddable and locatable spinning, is reported for the first time in this paper. Analysis of the key restrictions of the conventional and some novel ring-spinning were studied; evolvement and principles of embeddable and locatable spinning were then introduced. Analysis indicated that embeddable and locatable spinning could overcome the existing restrictions of ring spinning and improve the spinning performance of fiber strands as well as the quality of the resulting yarn. Super-fine and colorful figured yarns could be produced successfully, and most fibers shorter than can be spun in traditional spinning could be well embedded into a yarn by embeddable and locatable spinning method; even staple fibers of low qualities could be used to produce a fine yarn of high qualities in the novel spinning system. This novel spinning method shows huge application potentials in textile industry by improving the yarn quality, developing super-fine yarn, and increasing fiber utilization rate.

The ciao prolog system

Ciao is a public domain, next generation multi-paradigm programming environment with a unique set of features: Ciao offers a complete Prolog system, supporting ISO-Prolog, but its novel modular design allows both restricting and extending the language. As a result, it allows working with fully declarative subsets of Prolog and also to extend these subsets (or ISO-Prolog) both syntactically and semantically. Most importantly, these restrictions and extensions can be activated separately on each program module so that several extensions can coexist in the same application for different modules. Ciao also supports (through such extensions) programming with functions, higher-order (with predicate abstractions), constraints, and objects, as well as feature terms (records), persistence, several control rules (breadth-first search, iterative deepening, ...), concurrency (threads/engines), a good base for distributed execution (agents), and parallel execution. Libraries also support WWW programming, sockets, external interfaces (C, Java, TclTk, relational databases, etc.), etc. Ciao offers support for programming in the large with a robust module/object system, module-based separate/incremental compilation (automatically -no need for makefiles), an assertion language for declaring (optional) program properties (including types and modes, but also determinacy, non-failure, cost, etc.), automatic static inference and static/dynamic checking of such assertions, etc. Ciao also offers support for programming in the small producing small executables (including only those builtins used by the program) and support for writing scripts in Prolog. The Ciao programming environment includes a classical top-level and a rich emacs interface with an embeddable source-level debugger and a number of execution visualization tools. The Ciao compiler (which can be run outside the top level shell) generates several forms of architecture-independent and stand-alone executables, which run with speed, efficiency and executable size which are very competive with other commercial and academic Prolog/CLP systems. Library modules can be compiled into compact bytecode or C source files, and linked statically, dynamically, or autoloaded. The novel modular design of Ciao enables, in addition to modular program development, effective global program analysis and static debugging and optimization via source to source program transformation. These tasks are performed by the Ciao preprocessor ( ciaopp, distributed separately). The Ciao programming environment also includes lpdoc, an automatic documentation generator for LP/CLP programs. It processes Prolog files adorned with (Ciao) assertions and machine-readable comments and generates manuals in many formats including postscript, pdf, texinfo, info, HTML, man, etc. , as well as on-line help, ascii README files, entries for indices of manuals (info, WWW, ...), and maintains WWW distribution sites.

On embedding 4-manifolds in R^7

A compact, connected, combinatorial 4-maifold is embeddable in R^7 if its twisted normal Stiefel-Whitney class in dimension 3 is trivial.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.