Relevant bibliographies by topics / Testing Machine Company

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Testing Machine Company'

Author: Grafiati
Published: 25 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Testing Machine Company"

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Sibarani, Prince, Tanika D. Sofianti, and Aditya Tirta Pratama. "Improving the Overall Equipment Effectiveness (OEE) of Drum Testing Machine in Laboratory of Tire Manufacturing Using FMEA and PFMEA." Proceedings of The Conference on Management and Engineering in Industry 3, no. 3 (2021): 56–61. http://dx.doi.org/10.33555/cmei.v3i3.84.

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Drum testing is equipment to test tire capability in the highway prototype in the tire company. Overall Equipment Effectiveness (OEE) is used to measure the productivity of the equipment. OEE has declined and has not achieved the target from Jun 2019 until June 2020. The objectives of this research are to determine the fixed parameter in the OEE calculation at the Drum Testing and to increase the OEE for achieving the company target. Process Failure Mode Effects Analysis (PFMEA) and Failure Mode Effects Analysis (FMEA) help to identify potential failure mode and its consequences, and formulate
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Vijeta, Priya, and Vinila Jinny S. "An Investigation of Employee Attrition Prediction Using Machine Learning Algorithms." Research and Reviews: Advancement in Cyber Security 1, no. 1 (2023): 1–8. https://doi.org/10.5281/zenodo.10208409.

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<i>In an organization, employee attrition is a problem which is considered to be one of the most difficult to tackle. It is an established fact that, the employees are the major building blocks of any organization. Efficient employees add to the revenue, productivity, and efficiency of the company. But the companies face a major setback, when the employees start to leave the company, it rattles the HR people. It requires a bunch of dedicated and headstrong employees for any organization to flourish and produce effective results. So, it goes without saying that the employees are the most import
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C. M. Nalayani, Thanga Akilan .V, Hariharan .S, SaranArulnathan, and Venkatanathan .S. "Placement Analysis for Students using Machine Learning." September 2023 5, no. 3 (2023): 223–37. http://dx.doi.org/10.36548/jitdw.2023.3.001.

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Every University/College want their students to get placed in a good company with a better package. They create the syllabus so, that students will gain the most knowledge out of the study period. But they are not sure whether the students are getting trained with proper guidance and instruction to be placed. There is a need for metric to find the progress of the student’s placement. So, the student can speed up his/her preparation to meet the demands of the minimum criteria of placement. This metric is called the placement analysis system, it takes attributes like internships completed, paper
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Hayati, Mukti Asih, Kuan Eng Chong, and Mei Ph'ng Lee. "Simulation of mixed-load testing process in an electronic manufacturing company." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 1 (2019): 408–16. https://doi.org/10.12928/TELKOMNIKA.v17i1.10146.

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The automatic testing machine, called by mixed-load tester, has ability to load and test multiple product families in different testing durations simultaneously. However, the high product mixes for each product family undergoes a different process flow. In addition, the capability of the robot inside tester used for loading and unloading a product to each slot makes the capacity planning problem more complicated. It effects low tester utilization. This paper developed simulation models of capacity planning scenarios under demand and testing time uncertainty. These scenarios are built by robust
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Mustika, Nadya Intan, Bagus Nenda, and Dona Ramadhan. "Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company." Asia Pacific Fraud Journal 6, no. 2 (2021): 213. http://dx.doi.org/10.21532/apfjournal.v6i2.216.

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This study aims to implement a machine learning algorithm in detecting fraud based on historical data set in a retail consumer financing company. The outcome of machine learning is used as samples for the fraud detection team. Data analysis is performed through data processing, feature selection, hold-on methods, and accuracy testing. There are five machine learning methods applied in this study: Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Historical data are divided into two groups: training data and test data. The results sho
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Dos Santos Pereira, Alexandre, Marcelo Carneiro Gonçalves, Elpidio Oscar Benitez Nara, and Thales De Freitas Ferraz. "Application of machine learning methods in forecasting sales prices in a project consultancy." Produto & Produção 24, no. 2 (2023): 1–12. https://doi.org/10.22456/1983-8026.127422.

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The objective of this article is to apply a comparative analysis of machine learning techniques to predict project sales prices for a consulting company in the South of Brazil. The company is involved in various fields such as strategy, production, quality, and innovation. Due to this diverse range of projects, the company managers face challenges in accurately determining the sales value of new projects, as they deal with different types of predictor variables such as consultant type, project type, and number of hours. Hence, there is a need to utilize a method that can predict sales values t
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Efunogbon, Abimbola, Enjie Liu, Renxie Qiu, and Taiwo Efunogbon. "Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning." Future Internet 17, no. 2 (2025): 69. https://doi.org/10.3390/fi17020069.

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This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (
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Muhammad Yafi D and Rusindiyanto Rusindiyanto. "Perancangan Mesin Analisa Potongan Pellet Otomatis Menggunakan Software Blender Dengan Metode MDLC." Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika 3, no. 1 (2024): 128–38. https://doi.org/10.61132/jupiter.v3i1.672.

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PT XYZ) is a company which focuses on integrated aquaculture. Operating since 1987, PT XYZ has a fish and shrimp feed factory, fish and shrimp breeding and rearing as well as marine fish food processing and cold storage for local and global markets. At PT XYZ, especially the Quality Control division, there are still ineffective analysis activities, namely piece length analysis. An effort to increase productivity is by designing an automatic cut length analysis machine with the aim of maximizing efficiency and effectiveness. Therefore, the objective function of this research was carried out to
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Hasibuan, Agung Setiawan, Suendri Suendri, and Fathiya Hasyifah Sibarani. "Web Based Production Scheduling Information System for a Shrimp Paste Factory Utilizing the Waterfall Method." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 4 (2024): 1781–91. http://dx.doi.org/10.47709/cnahpc.v6i4.4801.

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Communication was essential in various aspects of life due to the rapid development of information technology, including in the management of educational institutions and companies. PT. Sumber Nelayan Indonesia faced issues in production scheduling, where the process was still done manually using bookkeeping. This caused delays in order fulfillment, primarily due to machine breakdowns and human resource challenges that impacted the production process. Additionally, the company struggled to manage order data and machine capacity, making it difficult to estimate order completion times and the am
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Guryanova, Lidiya S., Oleksiy S. Yevsyeyev, Oksana V. Panasenko, and Oleksandr O. Laptiev. "Business Decision-Making in the Video Game Market: Machine Learning Methods." Business Inform 12, no. 563 (2024): 147–53. https://doi.org/10.32983/2222-4459-2024-12-147-153.

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The article demonstrates that the product IT business, in particular the video game market, is characterized by high rates of development, which attracts an increasing number of companies to this segment and at the same time leads to increased competition that is global in its nature. This necessitates developing relevant decision-making models. The carried out analysis showed that at present considerable attention is paid to the use of machine learning methods in various circuits of managing the marketing activities of product IT companies, in particular gaming IT: strategic, tactical, operat
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Dissertations / Theses on the topic "Testing Machine Company"

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Olivier, Louis Petrus. "Psychomotor ability and learning potential as predictors of driver and machine operator performance in a road construction company." Diss., 2015. http://hdl.handle.net/10500/19687.

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The changing nature of work and its competitive characteristics are global phenomena and are mainly fuelled by ongoing technological advancement. This creates unique challenges for talent attraction and the retention of high performing individuals. In addition, the global workforce is becoming more diverse due to demographic, societal and cultural changes and companies are placing greater demands on employee competency and performance. Managing the human factor as a strategic asset in organisations remains a primary challenge in securing a competitive advantage. The road construction industry
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Book chapters on the topic "Testing Machine Company"

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Naumenko, Maksym, Iryna Hrashchenko, Tetiana Tsalko, Svitlana Nevmerzhytska, Svitlana Krasniuk, and Yurii Kulynych. "Innovative technological modes of data mining and modelling for adaptive project management of food industry competitive enterprises in crisis conditions." In PROJECT MANAGEMENT: INDUSTRY SPECIFICS. TECHNOLOGY CENTER PC, 2024. https://doi.org/10.15587/978-617-8360-03-0.ch2.

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Developed in this research scientific and practical applied project solutions regarding Data Mining for enterprises and companies (on the example of food industry) involve the application of advanced cybernetic computing methods/algorithms, technological modes and scenarios (for integration, pre-processing, machine learning, testing and in-depth comprehensive interpretation of the results) of analysis and analytics of large structured and semi-structured data sets for training high-quality descriptive, predictive and even prescriptive models. The proposed by authors multi-mode adaptive Data Mi
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Xhani, Donika, João Luiz Rebelo Moreira, Marten van Sinderen, and Luís Ferreira Pires. "XMLPO: An Ontology for Explainable Machine Learning Pipeline." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. https://doi.org/10.3233/faia241306.

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Machine Learning (ML) models often operate as black-boxes, lacking transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) aims to address the rationale behind these decisions, thereby enhancing the trustworthiness of ML models. In this paper, we propose an extension of the Explainable ML Workflows ontology, which was designed as a reference ontology with OntoUML, and implemented as an operational ontology with OWL. The Explainable ML workflows ontology reuses ML-Schema, which is a core ontology for representing ML algorithms. We have identified four main iss
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Abaei, Golnoush, and Ali Selamat. "Important Issues in Software Fault Prediction." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6026-7.ch023.

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Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different crite
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Klimaszewski, Piotr, Piotr Klonowicz, Łukasz Witanowski, et al. "Evaluation of the performance of an axial one-stage 10kW turbogenerator through experimental testing." In Proceedings of the 7th International Seminar on ORC Power System (ORC 2023), 2024th ed. Editorial Universidad de Sevilla, 2024. http://dx.doi.org/10.12795/9788447227457_25.

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The paper describes the results of experimental research on a prototype one-stage turbo-generator with a nominal power of 10 kW and a rotational speed of 24 000 rpm. The expander at the Institute of Fluid- Flow Machinery, Polish Academy of Sciences was developed as part of the R&amp;D project with Marani company. The unit construction consists of an overhanging rotor and shaft mounted on rolling bearings. The turbo-generator was tested on a specially constructed laboratory test stand in the first stage. Compressed air instead of refrigerant gas (R1233zd) was used. The test results converged wi
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Bhardwaj, Akashdeep. "Multinomial Naïve Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices." In Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256710124010007.

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Machine learning and artificial intelligence-based sentiment analysis are crucial for companies to automatically predict whether the customers are happy with their products. In this paper, a deep learning model is built to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company that has an online presence to automatically detect customer sentiment from their reviews. The objective of this research work is to propose a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive
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"Front Matter." In Pendulum Impact Machines: Procedures and Specimens for Verification. ASTM International100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959, 1995. http://dx.doi.org/10.1520/stp14652s.

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Comprised of the most current and wide-ranging information available on metallic and nonmetallic pendulum impact machines. Experts from all over the world have contributed 18 papers covering four major areas: The Specimen; • The Anvils and the Striker; • Establishing Reference Energies; • and Testing Procedures and Other Topics. Three papers compare the merits of two different radius striker designs. Several presentations focus on the development of standardized specimens and their impact on verification of machine performance. Machine and specimen tolerances are explored in five papers, cover
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Anand, Mansimar, Irtibat Shaukat, Harnoor Kaler, Jai Narula, and Prashant Singh Rana. "Hybrid Model for the Customer Churn Prediction." In SCRS Proceedings of International Conference of Undergraduate Students. Soft Computing Research Society, 2023. http://dx.doi.org/10.52458/978-81-95502-01-1-9.

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The fast development of the showcase in each segment is driving to a prevalent endorser base for benefit suppliers. In such a quick setup, benefit suppliers have realized the significance of holding the on-hand clients. It is in this manner fundamental for the benefits suppliers to prevent churn - a phenomenon that states that the client wishes to quit the benefit of the company. The key here is to be motivated and have interaction with these clients. While simple, in theory, the realities worried about achieving this “proactive retention” goal are incredibly challenging. The most commitment o
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Lukacic Zoran and Kern Josipa. "Consistency and reproducibility of attribute extraction by different machine learning systems." In Studies in Health Technology and Informatics. IOS Press, 2002. https://doi.org/10.3233/978-1-60750-934-9-316.

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Machine learning systems as tools for intelligent data analysis are used for extracting attributes relevant for prediction of defined outcomes. The aim of the paper was to compare two machine learning (ML) systems and propose method for intra- and inter-testing of consistency and reproducibility in attributes extracted from real dataset. See5 and FMLS extracted relevant attributes from real medical dataset. Comparison of results of both systems shows that: accuracy and sensitivity are nearly the same for both FMLS and See5 when FMLS was forced by attributes extracted by See5, and little bit lo
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Kanatani, Kenichi. "Statistical Analysis of Geometric Computation, 2." In Geometric Computation for Machine Vision. Oxford University PressOxford, 1993. http://dx.doi.org/10.1093/oso/9780198563853.003.0010.

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Abstract In this chapter, the techniques of statistical analysis established in Chapter 9 are applied to some of the most fundamental problems of machine vision. First, the reliability of focal length calibration is evaluated in statistical terms. We derive an optimal scheme for maximizing the reliability of the computed focal length and compute its confidence interval. Then, the error behaviours involved in 3-D motion estimation and conic fitting are analysed in detail. In each problem, the least-squares solution is shown to be statistically biased. We construct an unbiased estimation scheme
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Zhang, Xiang. "Financial Data Anomaly Recognition Model Based on Improved Support Vector Machine." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde231272.

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In order to achieve unsupervised classification of enterprise financial transaction data and identify suspicious abnormal financial data, a financial data anomaly recognition model based on support vector machine is proposed. This article adopts sample testing and independence testing to reduce the dimensionality of the company’s indicators, and selects principal component indicators that can accurately measure the financial condition of the enterprise. When the SVM model is introduced to solve the financial anomaly identification problem, the parameter optimization module are improved. Then,
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Conference papers on the topic "Testing Machine Company"

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Kamitani, Hiroki, Hisashi Amaya, Keiichi Kondo, and Yuji Arai. "Optimization of Side-Groove Configuration of NACE Method D Testing." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19218.

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Abstract NACE TM0177-2016 Method D testing is a common methodology to evaluate sulfide stress cracking performance in a sour environment. In this methodology, fracture surfaces are evaluated and some invalid cases are documented in accordance to TM0177 Method D 11.6.1. Specifically, edge cracks which occur from the bottom of side groove and prevents crack propagation is a key issue for this methodology and the optimization of side-groove configuration from V shaped side groove to U shaped side groove has been studied to prevent the occurrence. It was revealed that U shaped side groove can redu
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Binti Wahi, Siti Kathijah, Zurina Binti Jamil, Nadiah Binti Muhammad, Ahmad Mustaza Bin Ahmad Rusli, M. Faris Bin Mawardi, and Mohd Farid Bin Rahmat Sam. "Expert Corrosion Design Basis Memorandum (E-CDBM)-Corrosion Digital Solution at Your Fingertips." In MECC 2023. AMPP, 2023. https://doi.org/10.5006/mecc2023-19866.

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Abstract The objective of the paper is to communicate on the development and commissioned of Web-Based Online Expert Corrosion Design Basis Memorandum (E-CDBM), an online solution to accelerate, simplify &amp; standardize CDBM development which has been piloted for Upstream Wellhead Platforms and Pipelines. CDBM is a project key document consisting of the design basis of material selection, corrosion control, testing and monitoring which is typically developed and approved manually. This digitalization initiative will take CDBM to a next level in designing corrosion features for upstream facil
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Wika, Krystian K., Lee Waterhouse, Mark D. Callaghan, Charles Carpenter, David Coon, and Andrew Wright. "Machining Methods and Their Effect on the Fatigue Performance of SA508 Gr3 Class 2 and 316L Steels." In AM-EPRI 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.am-epri-2024p1183.

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Abstract Supercritical carbon dioxide cooling during machining has been identified as an effective measure to mitigate the risk of stress corrosion cracking in materials utilized in the primary circuit of light water reactors, particularly in pressure vessel structural steels. This study aims to compare two different cooling methods, the novel supercritical carbon dioxide and conventional high pressure soluble oil, employed during both milling and turning processes for SA508 Grade 3 Class 2 and AISI 316L steels. As the surface conditions of materials are critical to fatigue properties, such as
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Beavers, J. A., and C. E. Jaske. "Near-Neutral pH SCC in Pipelines: Effects of Pressure Fluctuations on Crack Propagation." In CORROSION 1998. NACE International, 1998. https://doi.org/10.5006/c1998-98257.

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Abstract Currently, there is a poor understanding of the effects of pressure related parameters (operating pressure, pressure fluctuations, and hydrostatic testings) on external stress corrosion crack propagation in pipelines in near-neutral-pH environments. A better definition of the role of these parameters on crack propagation is needed to aid in the prediction of crack growth rates on operating pipelines and to develop strategies to mitigate this form of cracking. The objective of the research described in this paper was to determine the roles and synergistic effects of operating pressure,
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Hiemenz, Gregory, Muthuvel Murugan, Wei Hu, Jin-Hyeong Yoo, and Norman Wereley. "Fail-Safe Magnetorheological Energy Absorber for a Vertically Stroking Crashworthy Seat Suspension." In Vertical Flight Society 70th Annual Forum & Technology Display. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9478.

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To mitigate injurious effects caused by crash loads, energy absorbing devices in crew seats are utilized to attenuate energy using a controlled seat stroke. The objective of this project is to develop a fail-safe magnetorheological energy absorber (FSMREA) with magnetic bias and demonstrate its ability in adapting stroking load based on occupant weight and crash severity. A multiple rotary vane structure is adopted to obtain a compact and lightweight FSMREA while maximizing energy absorbing capability required for maximum occupant weights. A design analysis is conducted to optimize a permanent
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Abbas, Raafat, Aaron Simon, Doug Dunbar, Rodrigo Serrano, Andrew Creegan, and Ernie Prochaska. "Automatic Driller Optimization Application Using Machine Learning and Artificial Intelligence Drives Consistent Performance in an Operator’s West Texas Drilling Program." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215087-ms.

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Abstract A large operator and drilling rig contractor worked collaboratively with a service company using a machine learning and artificial intelligence application to adjust setpoints on the auto driller to optimize performance in a West Texas drilling program. This application, which employed a novel usage of mud pulse telemetry, contributed to consistent performance in intermediate and lateral sections on multiple pads. The trial was conducted in the Midland Basin using Advisory Mode (manual) settings in the intermediate and lateral sections. The trial was considered successful with a decis
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Valdiero, Antonio C., Ronei O. Ziech, Mauricio S. Pinto, Ivan J. Mantovani, and Luiz A. Rasia. "Development and Construction of an Instrumentalized Workbench With a Hydraulic Motor for Farm Machine Testing." In 9th FPNI Ph.D. Symposium on Fluid Power. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fpni2016-1552.

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This work addresses the design and the construction of an instrumentalized workbench with hydraulic motor. The primary purpose of estimating the consumed power for farm machines, as for example the power consumption on the drive shaft of a variable rate fertilizer targeted for applications in precision agriculture. The estimate of the consumed power is important for the better design of the transmission elements and for machines control and design, collaborating for the reduction of the mass and the costs of the prototype. The instrumentalized workbench was used to estimate the actual consumed
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Gerchman, Mark Craig. "The Single Point Diamond Turning (SPDT) Of Optical Surfaces For Visible Wavelength Applications." In Optical Fabrication and Testing. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oft.1990.jtuc3.

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Recent advances in machine tools for single point diamond turning have significantly improved the quality of machined surfaces available. The most recent generation of SPDT machines are producing optical surfaces suitable for many visible wavelength applications. This presentation will examine the quality of current SPDT surfaces and compare them with reasonable criteria for visible wavelength surface specifications.
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Al Radhi, Mohammed, Fernando Angel Bermudez, Wael Al Madhoun, Khaled Al Blooshi, Noor Nasser Al Nahhas, and Mohammed Nazeih Shono. "Unlocking the Potential of Electrical Submersible Pumps: the Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization." In SPE Symposium: Artificial Intelligence - Towards a Resilient and Efficient Energy Industry. SPE, 2021. http://dx.doi.org/10.2118/208647-ms.

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Abstract This paper is a summary of the collaborative work between ADNOC (Abu Dhabi National Oil Company) and nybl, a deep tech development company, and the results of applying nybl's proprietary "Science-Based Artificial Intelligence" to ADNOC Electrical Submersible Pump (ESP) wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of Artificial Intelligence (AI) / Machine Learning (ML) in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maxim
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Bermudez, Fernando, Noor Al Nahhas, Hafsa Yazdani, Michael LeTan, and Mohammed Shono. "Unlocking the Potential of Electrical Submersible Pumps: The Successful Testing and Deployment of a Real-Time Artificially Intelligent System, for Failure Prediction, Run Life Extension, and Production Optimization." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/207839-ms.

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Abstract This paper is a summary of the collaborative work between a Gulf Cooperation Council (GCC) national oil company (NOC) and Nybl, a deep tech development company, and the results of applying Nybl's proprietary science-based AI to the GCC NOC ESP wells in real-time applications. The paper demonstrates the potential benefits of the real-life application of AI / Machine Learning in conjunction with traditional Petroleum Engineering concepts and algorithms to predict imminent and future failures, extend and monitor run life, and maximize the production of Electrical Submersible Pumps (ESP's
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Reports on the topic "Testing Machine Company"

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Ron, Alexa. Pilot Terrestrial Vegetation Monitoring in the Southeastern United States, 2009-2010 - Data Release Report. National Park Service, 2025. https://doi.org/10.36967/2303058.

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Data Release Reports (DRR) are created by the National Park Service and provide detailed descriptions of valuable research datasets in a human-readable format, including the methods used to collect the data and technical analyses supporting the quality of the measurements. DRRs focus on helping others reuse data rather than presenting results, testing hypotheses, or presenting new interpretations and in-depth analyses. Pilot terrestrial vegetation monitoring occurred in eleven Southeast Coast Network (SECN) parks in 2009 and 2010 and evaluated trends in plant cover, frequency, diversity and di
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Hobbs, Abigail, and Sarah Kelso. [PILOT] Herpetological Monitoring Surveys for the Southeast Coast Network: 2009-2010 - Data Release Report. National Park Service, 2025. https://doi.org/10.36967/2303227.

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Data Release Reports (DRR) are created by the National Park Service and provide detailed descriptions of valuable research datasets in a human-readable format, including the methods used to collect the data and technical analyses supporting the quality of the measurements. DRRs focus on helping others reuse data rather than presenting results, testing hypotheses, or presenting new interpretations and in-depth analyses. Pilot herpetological surveys were conducted in Cape Hatteras National Seashore, Cape Lookout National Seashore, Canaveral National Seashore, Congaree National Park, Cumberland I
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Veung, Naron, and Seyhah Ven. Exploring Insights into Vocational Skills Development and Industrial Transformation in Cambodia. Cambodia Development Resource Institute, 2021. https://doi.org/10.64202/wp.131.202110.

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Industrial development is central to Cambodia’s economic development as it plans to upgrade the country’s status to an upper-middle-income country by 2030 and to a high-income country by 2050. Equipping the workforce with the skills and competencies that match industrial needs requires a great deal of continuous efforts and resources. In this regard, this paper aims to look into the linkage between skills and transformation based on employers’ perspectives. It explores insights into how companies perceive and use the skills and qualifications of their employees to overcome changes in technolog
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