Multiple-input Model Research Articles

RaQuN: a generic and scalable n-way model matching algorithm

Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on text {N} input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.

Combinatorial Test Generation for Multiple Input Models With Shared Parameters

Combinatorial testing typically considers a single input model and creates a single test set that achieves <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> -way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.

Using Deep Learning Detection Of Arrhythmia

The field of deep learning applications is becoming more widespread. The use of traditional algorithms for arrhythmia detection is cumbersome and the algorithm complexity is relatively high. Using the deep learning model to directly input data into the model will make it difficult to effectively segment the data, which will have a large error in the recognition accuracy. We collected data from six commonly used ECG databases and then used the label shuffling method to amplify the samples, effectively overcoming the effects of sample imbalance. The LSTM model is used for feature point detection to effectively locate the key feature points of the ECG signal, thereby completing the segmentation and processing of the data. Finally, the use of a multiple input model single output for arrhythmia detection has achieved significant results. The average accuracy of the final arrhythmia classification of the model reached 0.711. The accuracy of detecting the fusion of ventricular and normal beat and the aberrated atrial premature beat exceeds 0.9.

Parallelism in the brain's visual form system.

We used magnetoencephalography (MEG) to determine whether increasingly complex forms constituted from the same elements (lines) activate visual cortex with the same or different latencies. Twenty right-handed healthy adult volunteers viewed two different forms, lines and rhomboids, representing two levels of complexity. Our results showed that the earliest responses produced by lines and rhomboids in both striate and prestriate cortex had similar peak latencies (40 ms) although lines produced stronger responses than rhomboids. Dynamic causal modeling (DCM) showed that a parallel multiple input model to striate and prestriate cortex accounts best for the MEG response data. These results lead us to conclude that the perceptual hierarchy between lines and rhomboids is not mirrored by a temporal hierarchy in latency of activation and thus that a strategy of parallel processing appears to be used to construct forms, without implying that a hierarchical strategy may not be used in separate visual areas, in parallel.

QA-RecombineIt: a server for quality assessment and recombination of protein models

QA-RecombineIt provides a web interface to assess the quality of protein 3D structure models and to improve the accuracy of models by merging fragments of multiple input models. QA-RecombineIt has been developed for protein modelers who are working on difficult problems, have a set of different homology models and/or de novo models (from methods such as I-TASSER or ROSETTA) and would like to obtain one consensus model that incorporates the best parts into one structure that is internally coherent. An advanced mode is also available, in which one can modify the operation of the fragment recombination algorithm by manually identifying individual fragments or entire models to recombine. Our method produces up to 100 models that are expected to be on the average more accurate than the starting models. Therefore, our server may be useful for crystallographic protein structure determination, where protein models are used for Molecular Replacement to solve the phase problem. To address the latter possibility, a special feature was added to the QA-RecombineIt server. The QA-RecombineIt server can be freely accessed at http://iimcb.genesilico.pl/qarecombineit/.

Preparing a dynamic model for water filtering process in Mosul city

In this research the scientific dynamic process is studied concering water filtering in Mosul city through employing the input and output variables represented by some tests which were performed on raw water before the filtering process, to be treated later through the numerous filtering stages. The tests involved: the electrical conduction, turbidity, water PH, and water temperature The same tests were conducted after the filtering process to guarantee that water is good for human consumption and to guarantee the validity of these tests as well and also to ensure that these tests meet the Iraqi Standard specifications before adding the chlorine. Four models were constructed through this study concerning the dynamic filtering process of the transfer functions with a single input for the four tests. And then models were built for the four transfer functions as input multiple series before filtering. Each of the four tests represented the output series by itself and consequently four models of transfer functions were obtained with multiple inputs and a single output variable in order to observe the range of impact for the output variable with the multiple input variables and using these models in Forecasting and then comparing the single input models and the multiple input models by means of using the standards of Forecasting control accuracy. The results showed that the electrical conduction model and the multiple input PH value were better than the results of the single input and the turbidity model of single input was better in comparison with the results of the multiple input. In addition to that, results showed that the temperature model for the single and the multiple input are similar.

Recollection-based memory in frontotemporal dementia: implications for theories of long-term memory.

It has been convincingly demonstrated that patients with semantic dementia (the temporal variant of frontotemporal dementia) can show intact recognition memory for pictorial stimuli. As yet, the contribution made by recollective processes to this ability and the status of associated neural regions have not been investigated in the disease. Here, we used both a source monitoring paradigm and an associative memory test to evaluate the ability of patients with semantic dementia to use recollection-based memory processes, and a volumetric MRI technique to assess the extent of atrophy in the hippocampus. Although some patients showed impaired source and associative memory, many performed as well as control participants. Importantly, status of semantic knowledge, as measured by tests of comprehension and production, did not predict recollection-based memory ability. There was no significant positive correlation between recollection and volume of the hippocampus; instead, both source discrimination and associative memory correlated highly with performance on a battery of frontal lobe tests. Consistent with the view that damage to the prefrontal cortex might influence recollection performance, patients with the frontal variant of frontotemporal dementia, with atrophy largely confined to the frontal lobes, all performed at floor level on source discrimination. These results provide further compelling evidence in favour of the multiple input model of long-term memory and highlight the role of frontal lobe systems in recollection-based memory.

WEDM-Adaptive Control with a Multiple Input Model for Indentification of Workpiece Height

The variation in power density during wire electrical discharge machining (WEDM) results in reduced productivity and wire rupture. This paper presents a WEDM adaptive control system that optimizes on-line the sparking frequency by estimating the workpiece height with a new multiple input model. This model describes the dynamic and stochastic relationships between average gap feedback voltage and spark frequency and machine table feed rate. Extensive experiments have been carried out to verify the new model. The proposed system correctly identifies the workpiece height and avoids the sudden rise in the estimated height found in existing system.

Dynamic structure of production under a quota: The case of milk production in the Federal Republic of Germany

A dynamic multiple output, multiple input model is estimated within the adjustment cost framework to analyze the economic behavior of dairy producers in the Federal Republic of Germany during the first half of the 1980s. The results suggest significant behavior given the introduction of the production quota. The changes in producers' responses were driven by changes in variable input and the evolution from a symmetric to asymmetric adjustment of quasi-fixed factors. In addition, German dairy farmers are found to exhibit considerable excess capacity in production. Coauthors are Jorge Fernandez-Cornejo, Conrado M. Gempesaw II, and Joachim G. Elterich. Copyright 1992 by Oxford University Press.

Development and application of multiple input models for structural noise source identification of forge hammers. Part I: Development

The application of multiple input models to analyze structurally generated noise from a forge hammer is discussed. Part I of this article is intended to present the rationale for application and analysis of multiple input models for noise source identification. The development of the empirical models is reviewed and investigated to show how the terms in the model can be interpreted to mathematically simulate the selective wrapping approach to source identification. The interaction of the structural excitation forces and radiated structural noise is examined for a four‐piece forge hammer and provides an indication of the characteristic measurements needed to develop the multiple input model that is representative of the hammer’s sound radiation. The transducer requirements for application to forge hammers are examined through experiments performed on a laboratory test structure and a forge hammer column. The results indicate that a single, well‐placed transducer may be sufficient to characterize the sound radiation from a monolithic element. Part II of this article analyzes the application of the techniques to a Chambersburg ♯8 die forger under production conditions. The experimental results indicate that the ram is the dominant source of sound energy, the columns are secondary sources, and the yoke and anvil are minor sources when detected through a microphone at the operator’s position.

Combination of Multiple Input Models and Experimental Modal Analysis for Identification of Structural Noise Generating Mechanisms: With Application to Forge Hammers

Combination of Multiple Input Models and Experimental Modal Analysis for Identification of Structural Noise Generating Mechanisms: With Application to Forge Hammers

On the statistical detection problem for multiple signals

The problem of detecting signals in noise is reviewed for the multiple input model, where each of the inputs can contain one of many possible signals. The detection procedure for this model becomes, in general, the testing of multiple hypotheses. Two detection criteria are examined for choosing among multiple hypotheses and it is found that, for both criteria, the decision is based on the likelihood functions for the various signals. Systems for computing likelihood ratios are examined in detail for the multiple input case. A multidimensional matched filter is considered and its relationship to the likelihood ratios is shown. Optimum signals are determined for the two-hypothesis problem.