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Relevant bibliographies by topics / Machine testing

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Academic literature on the topic 'Machine testing'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Machine testing"

1

Singh, Amandeep, and Harmanjit Singh. "Finite State Testing and Syntax Testing." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 48–54. http://dx.doi.org/10.24297/ijct.v3i1a.2725.

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This paper is concerned with the testing of the software which is being developed in a structured way. The advantages which accrue from a well-structured or modular organization of software depend upon an ability to independently test a module well before the full development of all the modules with which it communicates. This paper describes techniques (Finite State Testing & Syntax Testing) which effectively test various applications. With advanced computer technology, systems are getting larger to fulfill more complicated tasks, however, they are also becoming less reliable. Consequently, testing is an indispensable part of system design and implementation; yet it has proved to be a formidable task for complex systems. This motivates the study of testing finite state machines to ensure the correct functioning of systems and to discover aspects of their behavior. Finite state machines are widely used to model systems in diverse areas, including sequential circuits, certain types of programs, and, more recently, communication protocols. In a testing problem we have a machine about which we lack some information; we would like to deduce this information by providing a sequence of inputs to the machine and observing the outputs produced. Because of its practical importance and theoretical interest, the problem of testing finite state machines have been studied in different areas and at various times. Some old problems which had been open for decades were resolved recently, new concepts and more intriguing problems from new applications emerge. This paper reviews the fundamental problems in testing finite state machines and techniques for solving these problems, tracing progress in the area from its inception to the present and the state of the art. In addition, this paper covers syntax testing which is also called grammar based testing technique for testing various applications where the input data can be described formally.

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Semko, Roman. "Machine learning for robo-advisors: testing for neurons specialization." Investment Management and Financial Innovations 16, no. 4 (2019): 205–14. http://dx.doi.org/10.21511/imfi.16(4).2019.18.

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The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.

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Hashimoto, Toshimichi. "IGT's New Testing Machine." JAPAN TAPPI JOURNAL 57, no. 7 (2003): 1048–53. http://dx.doi.org/10.2524/jtappij.57.1048.

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Abbas Aghajanikalkhouran, Abbas Aghajanikalkhouran. "Volume Creep Testing Machine." International Journal of Mechanical and Production Engineering Research and Development 8, no. 1 (2018): 657–74. http://dx.doi.org/10.24247/ijmperdfeb2018072.

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Abbas Aghajanikalkhouran, Abbas Aghajanikalkhouran. "Volume Creep Testing Machine." International Journal of Mechanical and Production Engineering Research and Development 8, no. 1 (2018): 657–74. http://dx.doi.org/10.24247/ijmperdfeb201872.

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van Smaalen, Tim C., E. R. Pieter Hoogland, and L. W. Ernest van Heurn. "Machine perfusion viability testing." Current Opinion in Organ Transplantation 18, no. 2 (2013): 168–73. http://dx.doi.org/10.1097/mot.0b013e32835e2a1b.

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Yu, Wei Li. "Design of 80000N·m Torsion Testing Machine." Applied Mechanics and Materials 607 (July 2014): 350–53. http://dx.doi.org/10.4028/www.scientific.net/amm.607.350.

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The article introduce the mechanical structure and the principles of 80000 N·m torsion testing machine, the structure and the principles of checking power unit of this kind of testing machines and the control manner and the principles of the electronic control system, than expound the benefits of the equipment and its application.

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Ruth, Byron E., and Mang Tia. "Aggregate Degradation Testing by Gyratory Testing Machine." Transportation Research Record: Journal of the Transportation Research Board 1619, no. 1 (1998): 3–10. http://dx.doi.org/10.3141/1619-01.

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The Gyratory Testing Machine (GTM) equipped with an air roller was used to investigate the dry and wet degradation of six aggregates. Three gradings similar to those for the Los Angeles (LA) abrasion test were evaluated in the GTM by using 25, 50, 100, and 200 revolutions. The test data indicated substantial differences in gradation of the aggregates after testing. Type and initial size of the aggregate influenced the amount and characteristics of degradation. Regression analyses of the data resulted in the development of GTM degradation prediction equations using the LA abrasion loss. The most important goal for the GTM test results was to define gradation and the effect of moisture, instead of using only a relative indicator such as LA abrasion loss. Four aggregate blends were tested in the GTM to simulate degradation produced in handling and processing of aggregate through a drum mixer and drum dryer. These test results duplicated some of the gradations obtained after the material had passed through the drum.

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Belapurkar, P. D. "Development and Comparison of Manual Spring Testing Machine with Universal Testing Machine." IOSR Journal of Mechanical and Civil Engineering 02, no. 02 (2016): 12–16. http://dx.doi.org/10.9790/1684-15008020212-16.

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RAHMAN, Mizanur, and J. R. R. MAYER. "0216 Testing a Five Axis Machine Tool for Coordinate Metrology." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _0216–1_—_0216–6_. http://dx.doi.org/10.1299/jsmelem.2015.8._0216-1_.

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Dissertations / Theses on the topic "Machine testing"

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Liu, Mu-Hsin. "Multiaxial Fatigue Testing Machine." Ohio University / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1266241731.

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Zhang, Zheyu. "Machine Learning for Metamorphic Testing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235303.

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Test oracle is a mechanism used to validate all the functionalities ofsoftware under test. However, the lack of test oracle makes the processof software testing difficult. Metamorphic testing is a state of artapproach for automated software testing without test oracles basedon metamorphic relations. Metamorphic relations are a set of propertiesbetween inputs and outputs that a software could have. However,it is usually difficult to identify metamorphic relations for unknownprograms. This thesis aims for automatic generation of metamorphicrelations by utilizing machine learning algorithms with the methodrandom walk kernel using input from control flow graphs. By applyingKanawala et al. [1] previous work in our targeted system environment,we encountered a series of difficulties, which we also describein this thesis. It is important to introduce an alternative solution forour test suite that is working. The performance of our model is evaluatedby different measures including area under the receiver operatingcharacteristic curve and mean squared error. The results show promisingapplications of automatically predicting metamorphic relations forunknown programs. The study was conducted on software system inthe telecommunicaiton domain.<br>Testorakel är en mekanik som används till att avgöra om ett program är korrekt för all funktionalitet i program som testas. Metamorfisk test- ning är en toppmodärn metod för automatiserad programtesting vid saknad testorakel, kan använda metamofiska relationer istället. Tyvärr är det under normala förhållanden svårt att identifiera metamorfiska relationer för okända program. Denna avhandling ämnar därför att automatiskt generera metamorfiska relationer genom att använda ma- skininlärningalgoritmer med kärnmetoden slumpmässig gång och in- data från kontrollflödesgrafer. Metamorfiska relationer är en mängd egenskaper mellan indata och utdata som finns i alla program. Genom att replikera och applicera en tidigare studie av Kanewala et al. [1] i vår egen systemmålmiljö, har vi mött en del svårigheter som vi också be- skriver i denna avhandling. Det är viktigt att introducera alternativa lösningar för vår test svit som fungerar. Prestandan i vår modell ut- värderas av olika mätningar inkluderad AUC och MSE. Våra resultat verkar lovande att använda på automatisk predicerade metamorfiska relationer för okända program. Studien genomfördes på programsystem inom telekommunikationsområdet.

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Berry, Eric Dean. "Randomization testing of machine induced rules." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA304271.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, September 1995.<br>"September 1995." Thesis advisor(s): B. Ramesh, William J. Haga. Includes bibliographical references. Also available online.

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Weyrauch, Thomas. "The Paperboard Testing-Machine : Development Process." Thesis, KTH, Hållfasthetslära (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92639.

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The design of a paperboard testing machine, developed in order to analyse the mechanical behaviour of paperboard during the combined of out-of-plane shear and compressive loading as in the deepdrawing process, is presented. The methodology to design a paperboard testing machine is discussed and the most appropriate concepts are compared and evaluated. The design process is presented in detail, and some pilot tests are performed to give an overview about the functionality of the manufactured prototype.

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Cao, Bingfei. "Augmenting the software testing workflow with machine learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119752.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 67-68).<br>This work presents the ML Software Tester, a system for augmenting software testing processes with machine learning. It allows users to plug in a Git repository of the choice, specify a few features and methods specific to that project, and create a full machine learning pipeline. This pipeline will generate software test result predictions that the user can easily integrate with their existing testing processes. To do so, a novel test result collection system was built to collect the necessary data on which the prediction models could be trained. Test data was collected for Flask, a well-known Python open-source project. This data was then fed through SVDFeature, a matrix prediction model, to generate new test result predictions. Several methods for the test result prediction procedure were evaluated to demonstrate various methods of using the system.<br>by Bingfei Cao.<br>M. Eng.

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Lee, Dai Gil. "Manufacturing and testing of composite machine tool structures." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15265.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1985.<br>MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.<br>Vita.<br>Includes bibliographical references.<br>by Dai Gil Lee.<br>Ph.D.

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Bawaskar, Neerja Pramod. "Analog Implicit Functional Testing using Supervised Machine Learning." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2099.

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Testing analog circuits is more difficult than digital circuits. The reasons for this difficulty include continuous time and amplitude signals, lack of well-accepted testing techniques and time and cost required for its realization. The traditional method for testing analog circuits involves measuring all the performance parameters and comparing the measured parameters with the limits of the data-sheet specifications. Because of the large number of data-sheet specifications, the test generation and application requires long test times and expensive test equipment. This thesis proposes an implicit functional testing technique for analog circuits that can be easily implemented in BIST circuitry. The proposed technique does not require measuring data-sheet performance parameters. To simplify the testing only time domain digital input is required. For each circuit under test (CUT) a cross-covariance signature is computed from the test input and CUT's output. The proposed method requires a training sample of the CUT to be binned to the data-sheet specifications. The binned CUT sample cross-covariance signatures are mapped with a supervised machine learning classifier. For each bin, the classifiers select unique sub-sets of the cross-covariance signature. The trained classifier is then used to bin newly manufactured copies of the CUT. The proposed technique is evaluated on synthetic data generated from the Monte Carlo simulation of the nominal circuit. Results show the machine learning classifier must be chosen to match the imbalanced bin populations common in analog circuit testing. For sample sizes of 700+ and training for individual bins, classifier test escape rates ranged from 1000 DPM to 10,000 DPM.

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JOSE, MATHEW STEPHEN, and Vijay Francis. "Development, Validation and Implementation of Universal Testing Machine." Thesis, Tekniska Högskolan, Jönköping University, JTH, Industriell produktutveckling, produktion och design, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-50725.

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The thesis paper will be covering the grounds of what, how and why a Universal Testing Machine is used, an alternative to the generic Universal Testing Machinedesigns and how it is built. The primary objective of this thesis work is to designand build a Universal Testing Machine which can handle a load up to 10kN forpolymer materials which is cost effective and modular. The operating system and electronic components are open-source leaving room for further development and compression test. The mechanical properties of material can be measured through tensile test. It gives the the characteristic of tensile strength, yield strength, modulus of elasticity, ductility, resilience, and toughness. The thesis paper covers the use of real timeimage processing for calculating and plotting of a stress strain curve. It also covers the implementation of open source code, using a MATLAB user interface to control, analyse and compile the results of tests done using the new machine. These results have been compared to values obtained from a standard Universal Testing machine and thus validated. As the machine is modular, the parts can be swapped with better components that fits the requirement, leaving the possibility for easy upgrades in the future.

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Beriat, Pelin. "Non-destructive Testing Of Textured Foods By Machine Vision." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610405/index.pdf.

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In this thesis, two different approaches are used to extract the relevant features for classifying the aflatoxin contaminated and uncontaminated scaled chili pepper samples: Statistical approach and Local Discriminant Bases (LDB) approach. In the statistical approach, First Order Statistical (FOS) features and Gray Level Cooccurrence Matrix (GLCM) features are extracted. In the LDB approach, the original LDB algorithm is modified to perform 2D searches to extract the most discriminative features from the hyperspectral images by removing irrelevant features and/or combining the features that do not provide sufficient discriminative information on their own. The classification is performed by using Linear Discriminant Analysis (LDA) classifier. Hyperspectral images of scaled chili peppers purchased from various locations in Turkey are used in this study. Correct classification accuracy about 80% is obtained by using the extracted features.

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Stander, Melchior. "Development of an intermediate strain rate compression testing machine." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/9131.

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Includes bibliographical references.<br>The further development of the wedge bar Intermediate Strain rate Tester (IST) presented by Cloete and Oxtoby [1] is presented in this dissertation. The concept uses a wedge mechanism to deform 5 mm diameter, 5 mm long specimens at strain rates in the region of 10 s, up to strains of 30. As impact principles are used to start and stop the experiment, it takes less than 1 of the testing duration to reach testing speed and to stop the experiment. The kinetic energy stored in the wedge bar helps ensure a near constant loading rate over the duration of the experiment. The yield stress is captured at above 80 of the average strain rate for the experiment for most of the materials investigated. The design work focuses primarily around the design of the new load-frame, load-cell, loading platforms and wedge bar displacement sensor. Finite Element Analysis (FEA) was used to investigate the dynamic response of the IST and modify the design of components where necessary. The modifications made the load-frame and load-cell result in a significant improvement in the quality of the measured signals. The wedge bar displacement sensor performed well. The back pressure driven loading concept shows potential but needs more development before it is suitable for routine testing. A range of common engineering materials was tested at quasi static, intermediate and high strain rates and compared to the results found in the literature. The results match well, however, friction effects dominate the large strain response of the metal specimens. In its current configuration the IST is used effective for testing polymers up to large strains as well as yield stress and small strain measurement of metals. For materials for which specimen friction effects are a problem, interrupted tests can be used to extend the maximum strain achievable.

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Books on the topic "Machine testing"

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McEnroe, Barry. Machine tool trajectory testing and anlysis. University College Dublin, 1996.

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Kindermann, M. R. A slow strain-rate tensile testing machine. Dept. of Defence, Aeronautical Research Laboratory, 1989.

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Kelemu, Friew. Threshing-shelling machine of IAR. Institute of Agricultural Research, 1994.

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Porta, C. Testing of three coordinate measuring machine evaluation algorithms. Commissionof the European Communities, 1986.

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Jywe, W. A computer-aided accuracy testing device for machine tools. UMIST, 1992.

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Jaspal, Jasinder S. Surface testing and evaluation of the conveyor belt service machine. U.S. Dept. of the Interior, Bureau of Mines, 1988.

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Achmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. Nova Science Publishers, 2009.

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Hankel, Steve. Design of a hydraulic bending machine. U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 2004.

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Institution, British Standards. Guide for comparative testing of performance of detergents for domestic machine dishwashing. B.S.I, 1985.

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Pronikov, A. S. Programmnyĭ metod ispytanii͡a︡ metallorezhushchikh stankov. "Mashinostroenie", 1985.

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Book chapters on the topic "Machine testing"

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Schiller, Gary F. "Machine Performance Testing." In A Practical Approach to Scientific Molding. Carl Hanser Verlag GmbH & Co. KG, 2018. http://dx.doi.org/10.3139/9781569906873.005.

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Taylor, John T., and Wayne T. Taylor. "Unit Testing." In Patterns in the Machine. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6440-9_6.

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Paluszek, Michael, and Stephanie Thomas. "Multiple Hypothesis Testing." In MATLAB Machine Learning Recipes. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3916-2_12.

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McEnteggart, I., and R. D. Lohr. "Mechanical testing machine criteria." In Materials Metrology and Standards for Structural Performance. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-1264-2_2.

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Wang, Haiying, and Huiru Zheng. "Model Testing, Machine Learning." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_231.

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Gallardo-Alvarado, Jaime. "Gough’s Tyre Testing Machine." In Kinematic Analysis of Parallel Manipulators by Algebraic Screw Theory. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31126-5_12.

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Strug, Joanna, and Barbara Strug. "Machine Learning Approach in Mutation Testing." In Testing Software and Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34691-0_15.

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Amunategui, Manuel, and Mehdi Roopaei. "A/B Testing on PythonAnywhere and MySQL." In Monetizing Machine Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3873-8_14.

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Clarke, Bertrand, Ernest Fokoué, and Hao Helen Zhang. "Multiple Testing." In Principles and Theory for Data Mining and Machine Learning. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98135-2_11.

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Paluszek, Michael, and Stephanie Thomas. "Autonomous Driving with Multiple Hypothesis Testing." In MATLAB Machine Learning Recipes. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-3916-2_13.

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Conference papers on the topic "Machine testing"

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"HD5: machine testing." In Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference. IEEE, 2004. http://dx.doi.org/10.1109/imtc.2004.1351491.

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van Brug, Hedser, and O. W. Faehnle. "WAGNER: machine construction and operation considerations." In Optical Fabrication and Testing. OSA, 2000. http://dx.doi.org/10.1364/oft.2000.owa1.

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Robertson, Graham, and Ian Craw. "Testing Face Recognition Systems." In British Machine Vision Conference 1993. British Machine Vision Association, 1993. http://dx.doi.org/10.5244/c.7.3.

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Faehnle, Oliver, Christina Reynolds, Guoyu Yu, and David Walker. "Machine acceleration effects on computer controlled polishing." In Optical Fabrication and Testing. OSA, 2017. http://dx.doi.org/10.1364/oft.2017.om4b.1.

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Kang, Hyukmo, Henry Quach, Heejoo Choi, Greg Smith, and Dae Wook Kim. "Computational alignment of on-machine deflectometry." In Optical Manufacturing and Testing XIII, edited by Rolf Rascher and Dae Wook Kim. SPIE, 2020. http://dx.doi.org/10.1117/12.2576955.

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Sanathkumara, Kumarapelige N., and Samuel N. Cubero. "Automated soil hardness testing machine." In 2007 14th International Conference on Mechatronics and Machine Vision in Practice. IEEE, 2007. http://dx.doi.org/10.1109/mmvip.2007.4430747.

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Carvalho, Fernando D., F. Carvalho Rodrigues, Cassiano P. Pais, and Rogerio Prina. "Automatic Machine For Spin Testing." In Laser Technologies in Industry. SPIE, 1988. http://dx.doi.org/10.1117/12.968816.

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Moghadam, Mahshid Helali. "Machine learning-assisted performance testing." In ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 2019. http://dx.doi.org/10.1145/3338906.3342484.

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Iliopoulos, Athanasios, John G. Michopoulos, and John C. Hermanson. "Composite Material Testing Data Reduction to Adjust for the Systematic 6-DoF Testing Machine Aberrations." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71119.

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This paper describes a data reduction methodology for eliminating the systematic aberrations introduced by the unwanted behavior of a multiaxial testing machine, into the massive amounts of experimental data collected from testing of composite material coupons. The machine in reference is a custom made 6-DoF system called NRL66.3 and developed at the NAval Research Laboratory, that consists of multiple sets of hexapod configurations essentially forming a recursive synthesis of multiple parallel mechanisms. Hexapod linkages, the grips, and other deformable parts of the machine absorb energy. This is manifested in an either reversible or irreversible manner, thus introducing a parasitic behavior that is undesirable from the perspective of our ultimate goal of the material constitutive characterization. The data reduction focuses both on the kinematic (pose of the grip) and the reaction (forces and moments) that are critical input quantities of the material characterization process. The kinematic response is reduced by exploitation of the kinematics of the dots used for full field measurements. A correction transformation is identified by solving an inverse problem that minimizes the known displacements at the grips as given by the full field measurements and those given by the machine’s displacement sensors. A Procrustes problem formalism was introduced to exploit a known material behavior tested by the testing machine. Consequently, a correction transformation was established and was applied on the load cell data of the machine in order to eliminate the spurious responses appearing in the force and moment data.

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Salomie, Ioan, Tudor Cioara, Ionut Anghel, Mihaela Dinsoreanu, and Tudor Ioan Salomie. "Machine Simulation for Workflow Integration Testing." In Cluj-Napoca, Romania. IEEE, 2007. http://dx.doi.org/10.1109/iccp.2007.4352160.

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Reports on the topic "Machine testing"

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Lange, Fred F. Beta Site Testing of the SRI Stereolithography Machine. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada416673.

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Bawaskar, Neerja. Analog Implicit Functional Testing using Supervised Machine Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2097.

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Nicolson, David M., and Richard S. Sayles. Research on a Mini-Disc Machine for Gear Testing. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada195788.

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Yoder, R. C., W. L. Goodwin, and G. K. Werner. Machine reference mirror inspection by optical Fabry-Perot cavity testing. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6275455.

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Augenblick, Ned, Jonathan Kolstad, Ziad Obermeyer, and Ao Wang. Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27457.

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Helinski, Arthur L. Continued Simulation Testing of the MK19 MOD3 Machine Gun Support Kit. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199174.

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Nurprasetio, Pulung, Willy Kurnia, and Indra Nurhadi. Design and Manufacturing of Servo-Hydraulic Testing Machine for Rubber Engine Mounting. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0350.

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Geist, Jon. Machine-assisted human classification of segmented characters for OCR testing and training. National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.5105.

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Kiebel, G. R., J. E. Ellis, and M. R. Masliah. Usability testing of the human-machine interface for the Light Duty Utility Arm System. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10189690.

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Chou, Roger, Tracy Dana, and Kanaka D. Shetty. Testing a Machine Learning Tool for Facilitating Living Systematic Reviews of Chronic Pain Treatments. Agency for Healthcare Research and Quality (AHRQ), 2020. http://dx.doi.org/10.23970/ahrqepcmethtestingmachinelearning.

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