Dynamic Data Inputs Research Articles

G-PICS: A Framework for GPU-Based Spatial Indexing and Query Processing

Support for efficient spatial data storage and retrieval has become a vital component in almost all spatial database systems. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database systems could receive a large number of queries simultaneously. In this paper, we present a comprehensive framework named G-PICS for parallel processing of concurrent spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in a Multi-GPU environment to support datasets beyond the size of a single GPU’s global memory. Empirical evaluation of G-PICS shows significant performance improvement over the state-of-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X) and query processing (up to 80X).

Model based decision support system for land use changes and socio-economic assessments

Hydrological models are often linked with other models in cognate sciences to understand the interactions among climate, earth, water, ecosystem, and human society. This paper presents the development and implementation of a decision support system (DSS) that links the outputs of hydrological models with real-time decision making on social-economic assessments and land use management. Discharge and glacier geometry changes were simulated with hydrological model, water availability in semiarid environments. Irrigation and ecological water were simulated by a new commercial software MIKE HYDRO. Groundwater was simulated by MODFLOW. All the outputs of theses hydrological models were taken as inputs into the DSS in three types of links: regression equations, stationary data inputs, or dynamic data inputs as the models running parallel in the simulation periods. The DSS integrates the hydrological data, geographic data, social and economic statistical data, and establishes the relationships with equations, conditional statements and fuzzy logics. The programming is realized in C++. The DSS has four remarkable features: (1) editable land use maps to assist decision-making; (2) conjunctive use of surface and groundwater resources; (3) interactions among water, earth, ecosystem, and humans; and (4) links with hydrological models. The overall goal of the DSS is to combine the outputs of scientific models, knowledge of experts, and perspectives of stakeholders, into a computer-based system, which allows sustainability impact assessment within regional planning; and to understand ecosystem services and integrate them into land and water management.

The Design of Data Input Module in Virtual Welding System

By using inverse grating technology, improving the design of data input module in virtual welding system. We analyzed and accomplished the method of data input, taking into account the weldment needs to make a dynamic changes in the welding process, we design the static and dynamic data input modules. Through running some instances of the program, we have proved that, with the corresponding improvement of the inverse grating technology, the design of data input module in the virtual welding system has achieved good result.

DDDAS Computational Model and Environments

The Data Driven Applications Systems (DDDAS) concept entails “ the ability to dynamically incorporate data into an executing application simulation, and in reverse, the ability of applications to dynamically steer measurement processes”, creating “ application simulations that can dynamically accept and respond to ‘online’ field data and measurements and/or control such measurements”. Through the DDDAS concept, the application modeling capability, its accuracy and its efficiency, are enhanced over the traditional computational modeling methods, by complementing and augmenting the computational modeling with dynamic data inputs, which can be dynamically incorporated into the computation at runtime. In reverse, the ability of the executing application to steer the measurement processes, and conduct targeted measurements guided by the executing application, can result into more efficient and more effective measurement processes. By integrating the computational and measurement aspects of an application in a dynamic feed-back loop, DDDAS changes the paradigm of the traditionally distinct computational and measurement processes, and leads to a unification of the computing and instrumentation platforms of an application. The discussions in the present paper focus on a DDDAS computational model representing the unification of measurement and computational platforms, and on the new measurement capabilities enabled through DDDAS and in particular in architecting and dynamically managing sensor networks and the systems software needs for supporting the unified measurement and computational platforms in DDDAS environments.

Modeling and predictive control using fuzzy logic: Application for a polymerization system

AbstractIn this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010