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Journal articles on the topic 'Test input'

Author: Grafiati

Published: 28 June 2021

Last updated: 4 February 2022

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1

Keramati, Hossein, and Seyed-Hassan Mirian-Hosseinabadi. "Generating semantically valid test inputs using constrained input grammars." Information and Software Technology 57 (January 2015): 204–16. http://dx.doi.org/10.1016/j.infsof.2014.09.007.

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2

Andresen, BernhardH, and StanleyC Keeney. "4612499 Test input demultiplexing circuit." Microelectronics Reliability 27, no. 2 (January 1987): 396. http://dx.doi.org/10.1016/0026-2714(87)90316-7.

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3

Visser, Willem, Corina S. Pǎsǎreanu, and Sarfraz Khurshid. "Test input generation with java PathFinder." ACM SIGSOFT Software Engineering Notes 29, no. 4 (July 2004): 97–107. http://dx.doi.org/10.1145/1013886.1007526.

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4

Phan, Quoc-Sang. "Test input generation using separati logic." ACM SIGSOFT Software Engineering Notes 43, no. 4 (January 2, 2019): 55. http://dx.doi.org/10.1145/3282517.3302418.

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5

Pomeranz, Irith. "Low-power test sets under test-related primary input constraints." International Journal of Critical Computer-Based Systems 4, no. 3 (2013): 265. http://dx.doi.org/10.1504/ijccbs.2013.058396.

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6

Pomeranz, I., and S. M. Reddy. "Random Test Generation With Input Cube Avoidance." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 17, no. 1 (January 2009): 45–54. http://dx.doi.org/10.1109/tvlsi.2008.2001943.

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7

Wang, Zhiyu, Sihan Xu, Xiangrui Cai, and Hua Ji. "Test Input Selection for Deep Neural Networks." Journal of Physics: Conference Series 1693 (December 2020): 012017. http://dx.doi.org/10.1088/1742-6596/1693/1/012017.

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8

MA, Xiaoxing, Jue WANG, Chang XU, Yanyan JIANG, and Jian LU. "Automatic test-input generation for Android applications." SCIENTIA SINICA Informationis 49, no. 10 (October 1, 2019): 1234–66. http://dx.doi.org/10.1360/n112019-00003.

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9

Cseppentő, Lajos, and Zoltán Micskei. "Evaluating code-based test input generator tools." Software Testing, Verification and Reliability 27, no. 6 (February 6, 2017): e1627. http://dx.doi.org/10.1002/stvr.1627.

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10

Celik, Ahmet, Sreepathi Pai, Sarfraz Khurshid, and Milos Gligoric. "Bounded exhaustive test-input generation on GPUs." Proceedings of the ACM on Programming Languages 1, OOPSLA (October 12, 2017): 1–25. http://dx.doi.org/10.1145/3133918.

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11

Fleurey, Franck, Benoit Baudry, Pierre-Alain Muller, and Yves Le Traon. "Qualifying input test data for model transformations." Software & Systems Modeling 8, no. 2 (November 28, 2007): 185–203. http://dx.doi.org/10.1007/s10270-007-0074-8.

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12

Roy, Asim. "The hardest test for a theory of cognition: The input test." Behavioral and Brain Sciences 26, no. 5 (October 2003): 618–19. http://dx.doi.org/10.1017/s0140525x03350137.

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This commentary defines an additional characteristic of human learning. The nature of this test is different from the ones by Newell: This is a hard, pass/fail type of test. Thus a theory of cognition cannot partially satisfy this test; it either conforms to the requirement fully, or it doesn't. If a theory of cognition cannot satisfy this property of human learning, then the theory is not valid at all.

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13

White, Justin P., and Alessia Martini. "Frequency of Input." Instructed Second Language Acquisition 3, no. 1 (April 15, 2019): 3–27. http://dx.doi.org/10.1558/isla.36273.

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The present paper examines the effect of input at the macro- and micro-levels on beginning level language learners of Italian. At the macro-level, learners are exposed to Structured Input activities targeting Italian accusative clitics to determine their effect on learners' default processing strategies. At the microlevel, the effect of the number of target item tokens in the input is measured and subsequently compared: 40 and 80 tokens. This study included pre-test, immediate post-test and delayed post-test measures on interpretation and production to examine the effect of Structured Input activities. Findings reveal that Structured Input activities at varying token frequencies may affect learners' processing related to this target form as evidenced by their gains in interpretation, as well as their adoption of a 'second noun strategy' in which they overextend their altered processing strategy. As such, we discuss the theoretical and pedagogical ramifications of these findings.

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14

YASUDA, Chiaki, Hiroyuki FUYAMA, Shinichiro KAJII, and Makoto SAKUNO. "224 Computation of Input Energy to Test Structure on Shaking Table Test." Proceedings of the Dynamics & Design Conference 2003 (2003): _224–1_—_224–5_. http://dx.doi.org/10.1299/jsmedmc.2003._224-1_.

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15

Biao, Wu, and Qi-Wei Ge. "Test Input Data Generation for Choiceless Program Nets." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 1 (March 28, 2020): 37–45. http://dx.doi.org/10.37936/ecti-cit.2020141.197859.

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Software testing is an important problem in designing a large software system and this problem is difficult to be solved due to its computational complexity. We try to use program nets to approach this problem and have proposed algorithms to divide a whole program net into subnets that can structurally cover the original one based on divide-and-conquer method. This paper aims to solve the remaining task of our approach, that is how to find test input data for each subnet to be called choiceless program net. Firstly, definitions of program nets are extended and the properties of choiceless program nets are analysed. Secondly, polynomial algorithms are proposed to get all constraint conditions of any given choiceless program nets. Finally, a method to generate test input data under the obtained constraint conditions is proposed by adopting an SMT (Satisfiability Modulo Theories) solver, $Z3$ prover, and also an example is given to show the usefulness of our method.

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16

Heyes, C. M. "Imagination and imitation: Input, acid test, or alchemy?" Behavioral and Brain Sciences 19, no. 1 (March 1996): 131–32. http://dx.doi.org/10.1017/s0140525x0004190x.

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AbstractImmediate imitation is likely to be a major, direct input to Barresi & Moore's level 2 competence, but deferred imitation is unlikely to play a key role in the transition to level 3, because (1) the attribution of first person knowledge is neither a necessary cause nor an obvious consequence of deferred imitation, and (2) deferred imitation does not correlate phylogenetically with capacities that more plausibly either yield or reflect a concept of intentional agency.

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17

Schroeder, Patrick J., and Bogdan Korel. "Black-box test reduction using input-output analysis." ACM SIGSOFT Software Engineering Notes 25, no. 5 (September 2000): 173–77. http://dx.doi.org/10.1145/347636.349042.

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18

Pomeranz, I., and S. M. Reddy. "Test data compression based on input-output dependence." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 22, no. 10 (October 2003): 1450–55. http://dx.doi.org/10.1109/tcad.2003.818122.

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19

Kao, Tzu‐Cheng, Chung‐Yu Hung, Jian‐Hsing Lee, Chen‐Hsin Lien, Chien‐Wei Chiu, Kuo‐Hsuan Lo, Hung‐Der Su, and Wu‐Te Weng. "Failure mechanism for input buffer under CDM test." Electronics Letters 50, no. 9 (April 2014): 667–69. http://dx.doi.org/10.1049/el.2013.4094.

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20

Ringer, Talia, Dan Grossman, Daniel Schwartz-Narbonne, and Serdar Tasiran. "A solver-aided language for test input generation." Proceedings of the ACM on Programming Languages 1, OOPSLA (October 12, 2017): 1–24. http://dx.doi.org/10.1145/3133915.

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21

Rosner, Nicolás, Valeria Bengolea, Pablo Ponzio, Shadi Abdul Khalek, Nazareno Aguirre, Marcelo F. Frias, and Sarfraz Khurshid. "Bounded exhaustive test input generation from hybrid invariants." ACM SIGPLAN Notices 49, no. 10 (December 31, 2014): 655–74. http://dx.doi.org/10.1145/2714064.2660232.

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22

Fatimah, Khusnul Umi, Tarno Tarno, and Abdul Hoyyi. "PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)." Jurnal Gaussian 8, no. 4 (November 29, 2019): 451–61. http://dx.doi.org/10.14710/j.gauss.v8i4.26725.

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Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

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23

Sheng, Xiaoqin, Hans Kerkhoff, Amir Zjajo, and Guido Gronthoud. "ADC Multi-Site Test Based on a Pre-test with Digital Input Stimulus." Journal of Electronic Testing 28, no. 4 (August 2012): 393–404. http://dx.doi.org/10.1007/s10836-012-5309-0.

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24

Yubai, Kazuhiro, Shinya Terada, and Junji Hirai. "Stability Test for Multivariable NCbT Using Input/Output Data." IEEJ Transactions on Electronics, Information and Systems 131, no. 4 (2011): 773–80. http://dx.doi.org/10.1541/ieejeiss.131.773.

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25

OKITA, Tsuyoshi, Yasuhide KOBAYASHI, and Shogo TANAKA. "Adaptive Control for Linear Systems by Using Test Input." Transactions of the Society of Instrument and Control Engineers 27, no. 12 (1991): 1358–65. http://dx.doi.org/10.9746/sicetr1965.27.1358.

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26

Pomeranz, Irith. "Sequential Test Generation Based on Preferred Primary Input Cubes." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 36, no. 2 (February 2017): 351–55. http://dx.doi.org/10.1109/tcad.2016.2568210.

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27

Pomeranz, Irith. "Reducing the input test data volume under transparent scan." IET Computers & Digital Techniques 8, no. 1 (January 2014): 1–10. http://dx.doi.org/10.1049/iet-cdt.2013.0067.

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28

WANG, X. A., Y. F. XU, and W. C. LOU. "Solar Collector Parameter Identification by Step Input Test Procedure." International Journal of Solar Energy 4, no. 6 (January 1986): 327–34. http://dx.doi.org/10.1080/01425918608909868.

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29

Sun, Hanqiu. "A behavioural test-bed using a dataglove input device." Virtual Reality 1, no. 2 (December 1995): 109–16. http://dx.doi.org/10.1007/bf02009727.

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30

Del Grosso, C., G. Antoniol, E. Merlo, and P. Galinier. "Detecting buffer overflow via automatic test input data generation." Computers & Operations Research 35, no. 10 (October 2008): 3125–43. http://dx.doi.org/10.1016/j.cor.2007.01.013.

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31

Kuhn, D. Richard, Raghu N. Kacker, and Yu Lei. "Measuring and specifying combinatorial coverage of test input configurations." Innovations in Systems and Software Engineering 12, no. 4 (November 14, 2015): 249–61. http://dx.doi.org/10.1007/s11334-015-0266-2.

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32

Page, RobertE. "4658209 Universal test board, serial input (for synthesizer testing)." Microelectronics Reliability 27, no. 6 (January 1987): 1041. http://dx.doi.org/10.1016/0026-2714(87)90888-2.

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33

Dodier, Robert H., and Gregor P. Henze. "Statistical Analysis of Neural Networks as Applied to Building Energy Prediction." Journal of Solar Energy Engineering 126, no. 1 (February 1, 2004): 592–600. http://dx.doi.org/10.1115/1.1637640.

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It has been shown that a neural network with sufficient hidden units can approximate any continuous function defined on a closed and bounded set. This has inspired the use of neural networks as general nonlinear regression models. As with other nonlinear regression models, tools of conventional statistical analysis can be applied to neural networks to yield a test for the relevance or irrelevance of a free parameter. The test, a version of Wald’s test, can be extended to yield a test for the relevance or irrelevance of an input variable. This test was applied to the building energy use data of the Energy Prediction Shootout II contest. Input variables were selected by initially constructing a neural network model which had many inputs, then cutting out the inputs which were deemed irrelevant on the basis of the Wald test. Time-lagged values were included for some input variables, with the time lag chosen by inspecting the autocovariance function of the candidate variable. The results of the contest entry are summarized, and the benefits of applying Wald’s test to this problem are assessed.

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34

Toma, Alexandru, and Cristian Morarescu. "Detection of Short-Step Pulses Using Practical Test-Functions and Resonance Aspects." Mathematical Problems in Engineering 2008 (2008): 1–15. http://dx.doi.org/10.1155/2008/543457.

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An important aspect in modeling dynamic phenomena consists in measuring with higher accuracy some physical quantities corresponding to the dynamic system. Yet for measurements performed on limited time interval at high working frequency, certain intelligent methods should be added. The high working frequency requires that the measurement and data processing time interval should be greater than the time interval when the step input is received, so as to allow an accurate measurement. This paper will show that an intelligent processing method based on oscillating second-order systems working on limited time interval can differentiate between large step inputs (which are active on the whole limited time interval) and short step inputs (which are active on a time interval shorter than the limited working period). Some resonance aspects (appearing when the input frequency is close to the working frequency of the oscillating second-order system) will be also presented.

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35

Smallwood, David. "Minimum Input Trace for Multiple Input Multiple Output Linear Systems." Journal of the IEST 56, no. 2 (October 1, 2013): 57–67. http://dx.doi.org/10.17764/jiet.56.2.41344333010617q6.

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Specification of the cross spectra for a multiple-input/multiple-output (MIMO) vibration test is challenging. This paper presents a method for tests where the specifications of the output (the control points) autospectra are available. The autospectra of the outputs are specified and cross spectra between the outputs are derived that will minimize the trace of the autospectra of the inputs (the drive signals) with the constraint that the input spectral density matrix is positive definite. The hypothesis is that nature likes a minimum energy solution.

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36

Provost, Julien, Jean-Marc Roussel, and Jean-Marc Faure. "Generation of Single Input Change Test Sequences for Conformance Test of Programmable Logic Controllers." IEEE Transactions on Industrial Informatics 10, no. 3 (August 2014): 1696–704. http://dx.doi.org/10.1109/tii.2014.2315972.

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37

Pomeranz, Irith. "Use of input necessary assignments for test generation based on merging of test cubes." IET Computers & Digital Techniques 9, no. 2 (March 2015): 106–12. http://dx.doi.org/10.1049/iet-cdt.2014.0009.

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38

Pomeranz, Irith. "Combined input test data volume reduction for mixed broadside and skewed‐load test sets." IET Computers & Digital Techniques 10, no. 3 (May 2016): 138–45. http://dx.doi.org/10.1049/iet-cdt.2015.0117.

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39

Miropolsky, S., and S. Frei. "Reproducing system-level bulk current injection test in direct power injection setup for multiple-port DUTs." Advances in Radio Science 11 (July 4, 2013): 177–82. http://dx.doi.org/10.5194/ars-11-177-2013.

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Abstract. Many investigations have been published on the transferability of RF immunity test results between system and IC-levels. The RF signal level at DUT (Device under Test) inputs, i.e. either RF voltage amplitude or RF input current, is used as a reference value for the load on the DUT. Existing approaches analyze the DUT response as a function of the RF signal level at a single input pin, e.g. supply voltage. Sufficient accuracy of such an approach could be shown in several cases, but results are not sufficient as a general solution for complex DUT. This paper proposes both theoretical analysis and practical implementation of a DPI setup, where a disturbance, equivalent to system-level BCI setup, can be delivered to multiple DUT input ports.

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40

Azizi, Arash. "Effects of Non-negotiated Pre-modified Input, Negotiation of Input without Output, and Negotiation of Input plus Pushed Output on EFL Learners’ Vocabulary Learning." Journal of Language Teaching and Research 7, no. 4 (July 1, 2016): 773. http://dx.doi.org/10.17507/jltr.0704.19.

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This experimental study investigated the comparative effects of non-negotiated pre-modified input, negotiation of input without output, and negotiation of input plus pushed output on EFL learners’ comprehension and production. Before starting the study, forty-three male and female adult students at intermediate level took the Preliminary English Test (PET) and 30 of the students who scored one standard deviation above and below the mean score in the PET were randomly assigned to three experimental groups. The whole treatment took 10 sessions and, after the treatment, two sets of tests were administered; i.e., one written and the other oral. Analysis of Variance on comprehension test and analysis of nonparametric alternative, i.e., Kruskal-Wallis test, on production test, indicated that (a) negotiation had a positive effect on the comprehension and production of targeted L2 vocabulary items and (b) negotiation of input plus pushed output did not promote production of L2 vocabulary more than negotiation of input without output. The findings of this study provide empirical evidence on the important role of negotiation in facilitating comprehension and production of targeted L2 vocabulary items.

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41

Watanabe, Yuichi. "INPUT, INTAKE, AND RETENTION." Studies in Second Language Acquisition 19, no. 3 (September 1997): 287–307. http://dx.doi.org/10.1017/s027226319700301x.

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This study investigated the effects of text modification and task on incidental learning of foreign language vocabulary by reading. The focus of the study was on how different cue types (appositives and single and multiple-choice marginal glosses) and a task (translation) would affect processing of input, initial learning, and retention of the meaning of target words in a text. Two hundred thirty-one students in Japanese universities were randomly assigned to 10 condition groups. After a pretest, a reading task, and a cloze test, they were given a surprise vocabulary test. A week later, two unexpected delayed posttests were also given. The results indicated that (a) both single and multiple-choice marginal gloss conditions performed significantly better than the no cue and the appositive conditions, (b) no statistically significant difference existed between the single and the multiple-choice gloss conditions nor between the appositive and the no cue conditions, and (c) the translation task had no effect.

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42

Zhang, Xin, and Xiu Li Du. "Operational Modal Test of an Arch Bridge." Advanced Materials Research 446-449 (January 2012): 3233–38. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.3233.

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A modal test was carried out on a 120m span concrete filled steel tubular arch bridge under operational conditions. The moving traffic load is considered as uncorrelated white noises at various points of the road surface and the test is of the type of multi-input multi-output (MIMO). The modal parameters are identified by Global Rational Fraction Polynomials (GRFP) method, a frequency domain method. In order to taking the mobile nature of the traffic load into consideration, a multi-input NExT method is utilized to retrieve the frequency response function needed by GRFP. A proof of the NExT method in the case of multi-input case is also provided in this paper.

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43

Shelke, Sneha, and Sangeeta Nagpure. "Generation of String Test Input from Web using Regular Expression." International Journal of Computer Applications 99, no. 18 (August 20, 2014): 44–46. http://dx.doi.org/10.5120/17476-8375.

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44

Knudsen, Torben. "Preliminary Test for Nonlinear Input Output Relations in SISO Systems." IFAC Proceedings Volumes 33, no. 15 (June 2000): 175–80. http://dx.doi.org/10.1016/s1474-6670(17)39746-x.

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45

Morelli, Eugene A. "Flight Test Validation of Optimal Input Design Using Pilot Implementation." IFAC Proceedings Volumes 27, no. 8 (July 1994): 1007–12. http://dx.doi.org/10.1016/s1474-6670(17)47840-2.

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46

Zhang, Boyan, Deyu Li, and Chunlei Li. "Seismic input model of high slope and its test verification." IOP Conference Series: Earth and Environmental Science 304, no. 4 (September 18, 2019): 042010. http://dx.doi.org/10.1088/1755-1315/304/4/042010.

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47

Park, Ho Cheol, Su Whan Sung, and Jietae Lee. "Modeling of Hammerstein−Wiener Processes with Special Input Test Signals." Industrial & Engineering Chemistry Research 45, no. 3 (February 2006): 1029–38. http://dx.doi.org/10.1021/ie050540a.

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48

Behnamfar, Farhad, and Mohammadreza Najar. "Combined Dynamic Actuator–Shake Table Test with Optimized Input Energy." Journal of Engineering Mechanics 144, no. 3 (March 2018): 04018004. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001376.

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49

Bosscher, Peter J., and Dale R. Showers. "Effect of Soil Type on Standard Penetration Test Input Energy." Journal of Geotechnical Engineering 113, no. 4 (April 1987): 385–89. http://dx.doi.org/10.1061/(asce)0733-9410(1987)113:4(385).

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50

Duque-Carrillo, J. F., and R. Perez-Aloe. "High-bandwidth CMOS test buffer with very small input capacitance." Electronics Letters 26, no. 25 (1990): 2084. http://dx.doi.org/10.1049/el:19901343.

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