Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems ...
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Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central Control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.
We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
This paper addresses the problem of robust /spl Hscr//sub /spl infin// filtering for linear discrete-time systems subject to parameter uncertainties in the system state-space model and with multiple time delays in the...
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This paper addresses the problem of robust /spl Hscr//sub /spl infin// filtering for linear discrete-time systems subject to parameter uncertainties in the system state-space model and with multiple time delays in the state variables. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. A methodology is developed to design a stable linear filter that assures asymptotic stability and a prescribed /spl Hscr//sub /spl infin// performance for the filtering error, irrespective of the uncertainty and the time delays. The proposed design is given in terms of linear matrix inequalities, which has the advantage in that it can be implemented numerically very efficiently.
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict nume...
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ISBN:
(纸本)0780370449
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
The problem of robust H/spl infin/ filtering for continuous-time uncertain linear systems with multiple time-varying delays in the state variables is investigated. The uncertain parameters are supposed to belong to a ...
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The problem of robust H/spl infin/ filtering for continuous-time uncertain linear systems with multiple time-varying delays in the state variables is investigated. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. The aim is to design a stable linear filter assuring asymptotic stability and a prescribed H/spl infin/ performance level for the filtering error system, irrespective of the uncertainties and the time delays. Sufficient conditions for the existence of such a filter are established in terms of linear matrix inequalities, which can be efficiently solved by means of powerful convex programming tools with global convergence assured. An example illustrates the proposed methodology.
The problem of robust H∞ state-feedback control design for a class of continuous-time linear systems with Markovian jumping parameters and multiple time-varying delays at the states, and subject to uncertain paramete...
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This paper addresses the problem of robust H ∞ state-feedback control design for uncertain discrete-time linear systems with multiple time delays at the states and Markovian jumping parameters. The jumping parameters...
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This paper addresses the problem of robust H ∞ state-feedback control design for uncertain discrete-time linear systems with multiple time delays at the states and Markovian jumping parameters. The jumping parameters are assumed to be available and the uncertainties are supposed to belong to convex bounded domains (polytope type uncertainty). Delay-independent sufficient conditions assuring robust stochastic stability and a prescribed H ∞ disturbance attenuation for the closed-loop uncertain discrete-time linear system with multiple time delays and Markovian jumping parameters are established in terms of linear matrix inequalities, which have the advantage that can be implemented numerically veryefficiently.
The problem of robust H ∞ filter design for uncertain continuous-time linear systems with multiple time-delayed states is addressed in this paper. The uncertainties are assumed to belong to convex bounded domains (po...
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The problem of robust H ∞ filter design for uncertain continuous-time linear systems with multiple time-delayed states is addressed in this paper. The uncertainties are assumed to belong to convex bounded domains (polytope type uncertainty) and the time delays are supposed to be constant. Delay-independent as well as delay-dependent stability conditions assuring robust stability and a prescribed H ∞ disturbance attenuation for the filtering error system are established, in both cases, in terms of linear matrix inequalities, which can be efficiently solved by standard optimization procedures with global convergence assured. Two illustrative examples are analyzed.
We have proposed for the task of hourly electric load forecasting a hybrid neural system combining unsupervised and supervised learning. The system consists of a recurrent neural gas (RNG) network and many Elman neura...
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We have proposed for the task of hourly electric load forecasting a hybrid neural system combining unsupervised and supervised learning. The system consists of a recurrent neural gas (RNG) network and many Elman neural networks (ENs). RNG is a modification we introduced in the neural gas (NG) network in order to enable it to do clustering using a sequence of input data. For verifying the RNG's performance, many architectures are compared in the learning of global and local models. In a global model only one supervised network is trained and in a local model the training examples are grouped by a clustering algorithm and each one of these groups is sent to different supervised networks. These architectures use different clustering algorithms (NG and RNG) or different supervised networks for prediction (ENs that are trained by backpropagation or backpropagation through time, and feedforward networks).
作者：
Roos, CHCarl H. Roos:is a Senior Engineer with Logicon-Syscon. A graduate of the University of Pittsburgh with a BSEE degree
he has over 35 years experience in functional operational combat system fire control interface and computer program design. As technology changed and the combat system was upgraded Mr. Roos maintained his level of technical expertise by taking graduate-level courses in computer science modelling & structured analysis networking & fiber optics. He has worked in various capacities on the LHD LHA DDG 993 DD 963 LPD 17 LCC LPD 13 CGN 38 CGN 9 CG 26 and the DDG Class Combat Systems. In recent years Mr. Roos has been responsible for managing directing and performing engineering design and analysis efforts associated with Battle Management Organization (BMO) functional analysis and operational analysis. These efforts were used in defining combat system operational requirements shipboard space requirements and integration requirements. His paper “Configuration Management of Digital Programs” was published at the 1972 IEEE Southeastern Conrence.
NAVSEA 03K41 is responsible for generating Combat System Rattle Management Organizations (BMO) and Functional Flow Diagrams (FFD). Several years ago, NAVSEA provided the resources to conduct a functional analysis that...
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NAVSEA 03K41 is responsible for generating Combat System Rattle Management Organizations (BMO) and Functional Flow Diagrams (FFD). Several years ago, NAVSEA provided the resources to conduct a functional analysis that would support the development and validation of the BMOs and FFDs. The major obstacle in performing the analysis was obtaining a consensus on how the functional hierarchy was to be structured. The non-optimum organization of the hierarchy was selected;as a result, the functions were difficult to define, find, use, and validate. Recognizing the shortcomings of this effort, research was conducted to evaluate state-of-the-art structured modelling techniques, concepts, and methodologies. Two modelling concepts by James Martin were found to be applicable for the combat system functional analysis: Enterprise Modelling Concept and Functional Decomposition Modelling Concept. The Structure Modelling definitions of Whitten, Bently, and Barlow provided the guidelines for using the Martin concepts. During the ensuing BMO and FFD development efforts, a Ship's Combat System (SCS) Modelling concept evolved and a SCS Model was developed. This paper addresses how the modelling concepts and tools are used in the BMO and FFD development and validation process. Data from the SCS Model provides the basis for defining combat system requirements (e.g., software, data display, database, networking, etc.).
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