Parameters

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  • Getting Started Guide Is for quick commissioning with SDP and BOP. Operating Instructions Gives information about features of the MICROMASTER420, Installation, Commissioning, Control modes, System Parameter structure, Troubleshooting, Specifications and available options of the MICROMASTER420.

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  • [ Team LiB ] Recipe 4.9 Getting Stored Procedure Parameter Information at Runtime Problem You want to get information about the parameters used by a stored procedure at runtime. Solution Use DeriveParameters( ) method of the CommandBuilder.

    pdf4p luvpro 04-08-2010 69 12   Download

  • It is difficult to overemphasize the importance of magnetic resonance techniques in chemistry. Experimental spectra can usually be successfully interpreted empirically, but more difficult cases require a prediction based on the electronic structure. In the last 25 years the calculation of magnetic resonance parameters from first principles has become a powerful research tool that can significantly enhance the utility of magnetic resonance techniques when empirical interpretations are insufficient.

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  • Network Parameters in the Registry Networking features in Windows 2000, Windows XP, and Windows Server 2003 include several improvements

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  • Customizing the Boot Sequence and System-Behavior Parameters Most Windows operating systems automatically configure the default boot sequence.

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  • Supplying Parameters to Commands In the examples you've seen up to this point, the values for each column have been hardcoded in the SQL statements.

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  • [ Team LiB ] Recipe 2.14 Passing Null Values to Parameters Problem You need to pass a null value to a parameter. Solution Use the System.DbNull.Value static value. The sample code, as shown in Example 2-18, uses a single stored procedure

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  • This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...

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  • Weighted finite-state transducers suffer from the lack of a training algorithm. Training is even harder for transducers that have been assembled via finite-state operations such as composition, minimization, union, concatenation, and closure, as this yields tricky parameter tying. We formulate a “parameterized FST” paradigm and give training algorithms for it, including a general bookkeeping trick (“expectation semirings”) that cleanly and efficiently computes expectations and gradients. ...

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  • In this paper we will construct a parameter-dependent cyclic inequality that can be used to prove a lot of hard and interesting inequalities. 1. Introduction The cyclic inequality is a type of inequality that may be right in just some particular cases but not in genenal. In this paper, we propose one type of parameter-dependent cyclic inequality from a special inequality. Thanks to this inequality, we can obtain many inequalities by choosing α and n. Note that it can be proved by some ways in particular case. However in order to prove it in general case, we have to...

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  • This paper shows how to formally characterize language learning in a finite parameter space as a Markov structure, hnportant new language learning results follow directly: explicitly calculated sample complexity learning times under different input distribution assumptions (including CHILDES database language input) and learning regimes. We also briefly describe a new way to formally model (rapid) diachronic syntax change.

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  • Used to provide access to caller’s actual argument Caller’s data can be modified by called function! Typically used for input function To retrieve data for caller Data is then "given" to caller. Specified by ampersand, &, after type in formal parameter list

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  • Many scientific endeavours demand precise quantitative information on probabilistic properties of parameters of combinatorial objects. This chapter introduce to combinatorial parameters and MGFs. This chapter presents the following content: Basics, moment calculations, OBGF examples, labelled classes.

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  • Research objectives: Describe the clinical and subclinical characteristics in patients with Graves’ disease. Evaluation of Graves' disease treatment in children by anti-thyroid drug methimazole group synthesis. Survey and evaluate change TRAb levels and some clinical parameters, subclinical concerning treatment outcome and recurrence in children with Graves’ disease.

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  • (BQ) The present work deals with the comparison of the MRR for EN19 and EN41 material in a die sinking EDM machine. The various input factors like Pulse ON time, Pulse OFF time, Discharge current and voltage were considered as the input processing parameters, while the MRR is considered as the output. Optimization using Taguchi method was performed to predict the best combination of inputs towards maximum output.

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  • (BQ) The article presents an idea about the effect of the various input process parameters like Pulse ON time, Pulse OFF time, Discharge Current and Voltage over the Surface Roughness for an EN41 material. Here, 5 different output parameters concerned with surface roughness like Ra, Rq, Rsk, Rku and Rsm are taken and optimized accordingly, using the Grey-Taguchi method.

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  • (BQ) In this research work two different materials have been used as work pieces. These EN8 and D3 steel materials have been machined in an Electrical discharge machine which has wide application in Industry fields. The important process parameters that have been selected are peak current, pulse on time, die electric pressure and tool diameter. The outputs responses are material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR).

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  • (BQ) The present study discusses about having an overview of the EDM process, modeling of process parameters, and influence of process parameters such as input electrical variables, pulse shape, and discharge energy on performance measures such as material removal rate, surface roughness and electrode wear rate.

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  • (BQ) From the experimental results, it has been found that the electrical conductivity of the tool electrode has the most influencing nature on the machining characteristics in EDM process. The optimal combination of the input process parameters has been obtained using Taguchi-grey relational analysis.

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  • (BQ) This paper is devoted to a study of the influences of EDM parameters on surface roughness for machining of 40CrMnNiMo864 tool steel (AISI P20) which is widely used in the production of plastic mold and die. The selected EDM parameters were pulsed current (8, 16 and 24 A), pulse time (2, 3, 4, 6, 12, 24, 48 and 100 s) and pulse pause time (2 and 3s). It was observed that surface roughness of workpiece and electrode were influenced by pulsed current and pulse time, higher values of these parameters increased surface roughness.

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