59 resultados para Input saturation


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Thermal and visual comfort play a very important role regarding the satisfaction of occupants with their working environments. The most effective method to achieve thermal comfort in offices is to reduce cooling loads in order to avoid additional energy-consuming devices for cooling. Building simulation software can be a helpful tool for optimisation, and typically standard values for the influencing parameters are used in order to ensure compliance to norms and regulations.

In practice many of those parameters turn out to be different compared to the simulation assumptions and the reasons may be the chosen room or building related properties as well as the user behaviour influenced by the task and the corporate culture of the company.

This paper investigates exemplary for the climate of Hamburg, Germany and a naturally ventilated typical office room, the optimisation potential of the building- and user-related parameters for thermal comfort, daylighting and view when using realistic input data for building simulation. The study has been conducted with the EnergyPlus based simulation software “Primero-Komfort” [1].

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1. Waterbirds are considered to import large quantities of nutrients to freshwater bodies but quantification of these loadings remains problematic. We developed two general models to calculate such allochthonous nutrient inputs considering food intake, foraging behaviour and digestive performance of waterbirds feeding in terrestrial habitats: an intake model (IM), mainly based on an allometric relationship for energy requirements and a dropping model (DM), based on allometric relationships for defaecation.

2. Reviewed data of nitrogen (N) and phosphorus (P) content of herbivorous food varied according to diet type (foliage, seeds and roots), season and fertilization. For model parameterization average foliage diet contained 38.20 mg N g−1 and 3.21 mg P g−1 (dry weight), whereas mean faeces composition was 45.02 mg N g−1 and 6.18 mg P g−1.

3. Daily allochthonous nutrient input increased with body mass ranging from 0.29 g N and 0.03 g P in teals Anas crecca to 5.69 g N and 0.57 g P in mute swans Cygnus olor. Results from IM differed from those of DM from ducks to swans by 63–108% for N and by −4 to 23% for P. Model uncertainty was lowest for the IM and mainly caused by variation in estimates of food retention time (RT). In DM food RT and dropping mass determined model uncertainty in similar extent.

4. Exemplarily applying the models to Dutch wetlands resulted in mean annual contribution of herbivorous waterbirds to allochthonous nutrient loading of 382.8 ± 167.1 tonnes N a−1and 34.7 ± 2.3 tonnes P a−1, respectively, which corresponds to annual surface-water loadings of 1.07 kg N ha−1 and 0.10 kg P ha−1.

5. There was a distinct seasonal pattern with peak loadings in January, when bird abundances were highest. Lowest inputs were in August, when bird abundance and nutrient content in food was low and birds foraged less in terrestrial habitats. Three-quarters of all nutrient input was contributed by greater white-fronted goose Anser albifrons, greylag goose Anser anser, wigeon Anas penelope and barnacle goose Branta leucopsis alone.

6. We provide general, easy to use calculation methods for the estimation of allochthonous nutrient inputs by waterbirds, which are applicable to a range of waterbird species, a variety of potential diets and feeding behaviours, and across spatial scales. Such tools may greatly assist in the planning and execution of management actions for wetland nutrient budgets.

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This project uses methods of terrain representation, creation and realism described in literature. We find that using a combination of Fractional Brownian Motion and procedural formation of rivers via squig curves to form initial terrain, with hydraulic erosion for post processing, we have full control over the style of terrain: from jagged mountains to flat regions; and the phase of river from tightly rock controlled to flood plain regions.

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In this chapter the authors discuss the physical insight of the role of wireless communication in RFID systems. In this respect, this chapter gives a brief introduction on the wireless communication model followed by various communication schemes. The chapter also discusses various channel impairments and the statistical modeling of fading channels based on the environment in which the RFID tag and reader may be present. The chapter deals with the fact that the signal attenuations can be dealt with up to some level by using multiple antennas at the reader transmitter and receiver to improve the performance. Thus, this chapter discusses the use of transmit diversity at the reader transmitter to transmit multiple copies of the signal. Following the above, the use of receiver combining techniques are discussed, which shows how the multiple copies of the signal arriving at the reader receiver from the tag are combined to reduce the effects of fading. The chapter then discusses various modulation techniques required to modulate the signal before transmitting over the channel. It then presents a few channel estimation algorithms, according to which, by estimating the channel state information of the channel paths through which transmission takes place, performance of the wireless system can be further increased. Finally, the Antenna selection techniques are presented, which further helps in improving the system performance.


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This article considers the problem of estimating a partial set of the state vector and/or unknown input vector of linear systems driven by unknown inputs and time-varying delay in the state variables. Three types of reduced-order observers, namely, observers with delays, observers without internal delays and delay-free observers are proposed in this article. Existence conditions and design procedures are presented for the determination of parameters for each case of observers. Numerical examples are presented to illustrate the design procedures.

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In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.

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Constructing a monotonicity relating function is important, as many engineering problems revolve around a monotonicity relationship between input(s) and output(s). In this paper, we investigate the use of fuzzy rule interpolation techniques for monotonicity relating fuzzy inference system (FIS). A mathematical derivation on the conditions of an FIS to be monotone is provided. From the derivation, two conditions are necessary. The derivation suggests that the mapped consequence fuzzy set of an FIS to be of a monotonicity order. We further evaluate the use of fuzzy rule interpolation techniques in predicting a consequent associated with an observation according to the monotonicity order. There are several findings in this article. We point out the importance of an ordering criterion in rule selection for a multi-input FIS before the interpolation process; and hence, the practice of choosing the nearest rules may not be true in this case. To fulfill the monotonicity order, we argue with an example that conventional fuzzy rule interpolation techniques that predict each consequence separately is not suitable in this case. We further suggest another class of interpolation techniques that predicts the consequence of a set of observations simultaneously, instead of separately. This can be accomplished with the use of a search algorithm, such as the brute force, genetic algorithm or etc.

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In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

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This article presents necessary and sufficient conditions for the existence and design of an unknown input Functional observer. The existence of the observer can be verified by computing a nullspace of a known matrix and testing some matrix rank conditions. The existence of the observer does not require the satisfaction of the observer matching condition (i.e. Equation (16) in Hou and Muller 1992, ‘Design of Observers for Linear Systems with Unknown Inputs’, IEEE Transactions on Automatic Control, 37, 871–875), is not limited to estimating scalar functionals and allows for arbitrary pole placement. The proposed observer always exists when a state observer exists for the unknown input system, and furthermore, the proposed observer can exist even in some instances when an unknown input state observer does not exist.

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This paper considers the exponential stabilization problem via static and dynamic output feedback controllers of linear systems with a time delay in both the state and input. By using a change of the state variable and combining with the Lyapunov-Krasovskii method, new sufficient conditions for exponential stabilization via static and dynamic output feedback controllers are proposed. The conditions are expressed in terms of matrix inequalities but with only one parameter needs to be tuned and therefore can be efficiently solved by incorporating an one-dimensional search method into the Matlab’s LMI toolbox. Two numerical examples are provided to illustrate the obtained results.

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In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.

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The development of new quantitative magnetic resonance imaging (MRI) technologies open new opportunities for measurements of mass transport in porous media. The current work examines a simple miscible displacement process of H2O and D2O in porous media samples. Laboratory measurements of dispersion in porous media traditionally monitor the effluent intensity of an injected tracer. We employ MRI to obtain quantitative water saturation profiles, and to measure dispersion in rock core plugs. The saturation profiles are modeled with PHREEQC, a fluid transport modeling program. We demonstrate how independent magnetic resonance measurements can be employed to estimate three important input parameters for PHREEQC, mobile porosity, immobile porosity, and dispersivity. Bulk Carr Purcell Meiboom Gill (CPMG) T2 distribution measurements were undertaken to estimate mobile and immobile porosity. Bulk alternating-pulsed-gradient-stimulated-echo (APGSTE) measurements were undertaken to measure dispersivity. The imaging method employed, T2 mapping Spin Echo Single Point Imaging (SE-SPI), also provides information about the pore size distributions in the rock cores, and how the fluid occupancy of the pores changes during the displacement process.