A technique for implementing a set processor

Extended set theory is an effective method for describing the complex data structures needed to support large-scale data base applications. This paper describes the incorporation of the salient concepts of extended set theory into a general tool for the design and implementation of large-scale database systems. This tool is called a set processor. This implementation is based on the representation of sets of positive integers as bit strings and the application of the Cauchy/Cantor Diagonal Method.

This paper presents the design and implementation of a deductive database system. RDL1, is the production rule language for this system. This language is an extension of logic based languages towards the support of updates and complex domains. Different features of this language are described with its applications. The general architecture of the system is presented highlighting integration with a relational database system. Then, three original features of the system are discussed: extensibility through the definition of end user data types, query optimization techniques, and the extensibility of the control strategy. Future improvements and extensions conclude the paper.

Measurement-driven dashboards provide a unifying mechanism for understanding, evaluating, and predicting the development, management, and economics of large-scale systems and processes. Dashboards enable interactive graphical displays of complex information and support flexible analytic capabilities for user customizability and extensibility. Dashboards commonly include software requirements and design metrics because they provide leading indicators for project size, growth, and stability. This paper focuses on dashboards that have been used on actual large-scale projects as well as example empirical relationships revealed by the dashboards. The empirical results focus on leading indicators for requirements and design of large-scale systems. In the first set of 14 projects focusing on requirements metrics, the ratio of software requirements to source-lines-of-code averaged 1:46. Projects that far exceeded the 1:46 requirements-to-code ratio tended to be more effort-intensive and fault-prone during verification. In the second set of 16 projects focusing on design metrics, the components in the top quartile of the number of component internal states had 6.2 times more faults on average than did the components in the bottom quartile, after normalization by size. The components in the top quartile of the number of component interactions had 4.3 times more faults on average than did the components in the bottom quartile, after normalization by size. When the number of component internal states was in the bottom quartile, the component fault-proneness was low even when the number of component interactions was in the upper quartiles, regardless of size normalization. Measurement-driven dashboards reveal insights that increase visibility into large-scale systems and provide feedback to organizations and projects.

Medical database security plays an important role in the overall security of medical information systems and networks. This is both because of the nature of this technology and its widespread use today. Database security not only involves fundamental ethical principles, but also essential prerequisites for effective medical care. The development of appropriate secure medical database design and implementation methodologies is an important research problem in the area and a necessary prerequisite for the successful development of such systems. The general framework and requirements for medical database security are given and a number of parameters of the secure medical database design and implementation problem are presented and discussed in this paper. A secure medical database development methodology is also presented which could help overcome some of the problems currently encountered.

: Controls research under this program concentrated on the development of a network thermodynamic stabilization framework for hybrid control design of large-scale and multiagent aerospace systems. In particular, we concentrate on hybrid control, hierarchical control, impulsive dynamical systems, nonnegative dynamical systems, compartmental systems, large-scale systems, nonlinear switching control, cooperative control, and adaptive control. Application areas include large flexible interconnected space structures, spacecraft stabilization, cooperative control of unmanned air vehicles, network systems, swarms of air and space vehicle formations, and pharmacological systems.

This paper investigates the problem of distributed piecewise filtering for discrete-time large-scale nonlinear systems. The considered large-scale system is composed of a number of nonlinear subsystems and exchanges its information through communication network. Each nonlinear subsystem is described by a Takagi-Sugeno (T-S) model, and data-packet dropouts happen intermittently in communication network, and its stochastic variables are assumed to satisfy the Bernoulli random-binary distribution. Our objective is to design a distributed piecewise filter such that the filtering error system is stochastically stable with an performance. Based on a piecewise Lyapunov function and some convexifying techniques, less conservative results are developed for the distributed piecewise filtering design of the considered system in the form of linear matrix inequalities (LMIs). The effectiveness of the proposed method is validated by two examples.

Modeling of physical systems with high accuracy results into a complex model with higher-order dynamics. This creates intricacy in analysis, simulation and controller design. So one needs to find an approximate simpler representation of such complex systems while preserving the characteristic properties of the higher order system. Reduced lower order representations provide facility in easy analysis of such systems by reduction in computational complexity and faster simulation. In the present paper, a numerically efficient approximation method such as balanced truncation has been applied to obtain the small scale linear time invariant system. Here results of approximation are then evaluated through integral square error. Finally controller has also been designed for the reduced dimension representation of large scale systems. Few numerical problems have been tested to show the effectiveness of the approach.

We describe the design and implementation of the Glue-Nail deductive database system. Nail is a purely declarative query language; Glue is a procedural language used for non-query activities. The two languages combined are sufficient to write a complete application. Nail and Glue code are both compiled into the target language IGlue. The Nail compiler uses variants of the magic sets algorithm and supports well-founded models. The Glue compiler's static optimizer uses peephole techniques and data flow analysis to improve code. The IGlue interpreter features a run-time adaptive optimizer that reoptimizes queries and automatically selects indexes. We also describe the Glue-Nail benchmark suite, a set of applications developed to evaluate the Glue-Nail language and to measure the performance of the system.

The music database system based on data mining algorithms is an intelligent system based on large scale music datasets, aiming to analyze, classify, and recommend music through data mining technology. This article proposes a methodology for designing and implementing the system. Firstly, the system uses a large scale music dataset and uses data preprocessing techniques to clean and organize the music data for subsequent mining and analysis. Then, the system introduces various data mining algorithms, such as clustering, association rule mining, classification, etc., to extract useful information and features from music data. These mining algorithms can help the system achieve functions such as music style classification, sentiment analysis, and recommendation. Secondly, the system has designed a front end interface for user interaction and query, through which users can search, browse, and manage songs in the music database. The system also provides personalized recommendation function, which recommends relevant music works based on user preferences and historical behavior.During the implementation process, we used the Python programming language and common data mining tools and algorithm libraries. The system architecture includes modules such as data preprocessing, feature extraction, model training, and user interface. Through the collaborative work of these modules, a fully functional music database system has been achieved. The experimental results show that the system can effectively process music data, extract key features and information of music, and provide personalized recommendations based on user needs and preferences. User feedback on the system also indicates that the system has a certain degree of accuracy and practicality in music classification and recommendation.

We describe the design and implementation of the Glue-Nail deductive database system. Nail is a purely declarative query language; Glue is a procedural language used for non-query activities. The t...

Logical design of data base systems

In this paper, the authors propose an optimal IMC-PID controller design for the Load Frequency Control (LFC) of large-scale power system via model approximation method. The model approximation method uses the Enhanced Differential Evolution (EDE) algorithm to determine an optimal Reduced Order Model (ROM) for the considered large-scale power system by minimizing the performance measure called Integral Square Error (ISE) between their step responses. Later, the LFC design is carried out using an optimal ROM instead of processing with the large-scale power system model. Thus, this simplifies the design, reduces the computational efforts and also helps in determining the lower order controller. An optimal IMC design methodology is proposed by minimizing ISE between the actual output and the reference input responses of the large-scale power system using EDE algorithm. Further, PID controller gains are acquired by least square model matching with the optimal IMC transfer function. The proposed IMC-PID controller design allows a satisfied reference input tracking performance, robustness in disturbance rejection and improves the dynamic stability of the power system. The proposed method is validated by applying it to a single area power system of third-order SISO model and also extended to a centralized two-area thermal–thermal non-reheated power system of a seventh-order MIMO model. The simulation results and the comparison of error performance indices show the efficacy of the proposed method over the significant methods available in the literature.

Decentralized piecewise [formula omitted] fuzzy filtering design for discrete-time large-scale nonlinear systems with time-varying delay

: Modeling large scale systems with natural and artificial components requires storage of voluminous amounts of knowledge/information as well as computing speed for simulations to provide reliable answers in reasonable time. Computing technology is becoming powerful enough to support such high performance modeling and simulation. This report proposes a high performance simulation based optimization environment to support the design and modeling of large scale systems with high levels of resolution, and represents the results of contract F3O602-95-C-O25O, Methodology for Simulation Model Abstraction.

Hybrid analog and digital beamforming is a promising technique for large-scale MIMO systems since it can achieve a performance close to the performance of the conventional fully-digital beamforming schemes, but with much lower hardware implementation complexity and power consumption. One of the major challenges in hybrid beamforming is to design the hybrid beamformers for broadband systems with frequency-selective channels. This is because in broadband systems, it is desirable to design the same analog beamformers for the entire band while adapting digital beamformers in each frequency tone. In this paper, we consider the hybrid beamforming design for large-scale MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation. Specifically, we propose a unified heuristic design for two different hybrid beamforming structures, the fully-connected and partially-connected structures, to maximize the overall spectral efficiency of a broadband system. Numerical results show that the proposed algorithm outperforms the existing hybrid beamforming designs and further the proposed algorithm for the fully-connected structure can achieve spectral efficiency close to that of the optimal fully-digital solution with much less number of RF chains.