Relevant bibliographies by topics / CRITICAL MACHINE ENERGY

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  1. Journal articles

Academic literature on the topic 'CRITICAL MACHINE ENERGY'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "CRITICAL MACHINE ENERGY"

1

Chen, Chi, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, and Shyue Ping Ong. "A Critical Review of Machine Learning of Energy Materials." Advanced Energy Materials 10, no. 8 (2020): 1903242. http://dx.doi.org/10.1002/aenm.201903242.

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2

Ohtani, Hisashi. "Development of Energy-Saving Machine Tool." International Journal of Automation Technology 11, no. 4 (2017): 608–14. http://dx.doi.org/10.20965/ijat.2017.p0608.

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Environmental measures are urgently required to realize a society with a low environmental load. In response, various undertakings are being carried out in the field of machine tools, which is most critical in terms of requirement of energy-saving measures. The energy consumed when a machine tool is used to machine can be broadly divided into three categories: the “standby energy” to maintain the electrical devices operational when the machine is not operational; the “steady-state energy,” which is the fixed amount of energy required when the machine is in operation; and the “dynamic energy,” which varies with the machining conditions and other factors. Measures are necessary for each of these energy categories to reduce energy consumption. This paper describes an example of an energy-saving study undertaken for a machine tool used for machining gears; a new processing method called skiving was developed to consolidate work processes.
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Fujishima, Makoto, Hiroshi Shimanoe, and Masahiko Mori. "Reducing the Energy Consumption of Machine Tools." International Journal of Automation Technology 11, no. 4 (2017): 601–7. http://dx.doi.org/10.20965/ijat.2017.p0601.

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Global warming is one of the most important environmental issues that the world faces today. Reducing energy consumption is critical in industrial environments. Machine tools have some of the highest energy consumption rates of all the equipment in factories. This makes it important to reduce machine tool energy consumption to protect the global environment. Some effective ways of reducing the energy consumption of machine tools are by reducing the required energy, shutting down the power to standby mode, and shortening cycle times. This paper introduces several approaches to the reduction of energy consumption.
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Yuan and Sun. "Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing." Sensors 19, no. 12 (2019): 2724. http://dx.doi.org/10.3390/s19122724.

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High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.
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Alghamdi, Noof Awad, Israa Mohammed Budayr, Samar Mohammed Aljehani, and Majed Mohammed Aborokbah. "A Scheme for Predicting Energy Consumption in Smart Cities Using Machine Learning." Webology 19, no. 1 (2022): 3481–99. http://dx.doi.org/10.14704/web/v19i1/web19230.

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Fluctuating result on weather condition throughout several decades became a global concern due to the direct or indirect effect on energy consumption, and that was well-defined in several sector. Research investigates the use of technology and the speed of obtaining information ، which helps in decision-making. This paper Emphasize the role of data science and their application to monitoring energy consumption, also, explain the importance used and challenges of Internet of Things (IoT). Thus, there is a global concern on data transformation from IoT devices when taking into account deferent weather variations. Cities are a critical part when of energy management, it presents the effect of urbanization and some of the success achievement in several cities around the world. Our Analysis indicate that three dissimilar types of sensors can detect massive amount of information up to four hundred thousand rows, compared to traditional methods for collecting data. The results depict the resilient of IOT performance which provide an aggregate of measures reach around 405,184 rows in a record time, with achieved accuracy up to 99% when implementing the decision tree algorithm, the outcome after applying the algorithm was vary 27.60 per-cent recorded by the first device while the other devices scored 26.14%,46.26% respectively, throughout different circumstances with continuous reading in a short period of times around 8 days.
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Kandil, Abdelrahman, Samir Khaled, and Taher Elfakharany. "Prediction of the equivalent circulation density using machine learning algorithms based on real-time data." AIMS Energy 11, no. 3 (2023): 425–53. http://dx.doi.org/10.3934/energy.2023023.

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<abstract> <p>Equivalent circulation density (ECD) is one of the most important parameters that should be considered while designing drilling programs. With increasing the wells' deep, offshore hydrocarbon extraction, the costly daily rate of downhole measurements, operating restrictions, and the fluctuations in the global market prices, it is necessary to reduce the non-productive time and costs associated with hole problems resulting from ignoring and incorrect evaluation of ECD. Therefore, optimizing ECD and selecting the best drilling parameters are curial tasks in such operations. The main objective of this work is to predict ECD using three machine learning algorithms: an artificial neural network (ANN) with a Levenberg-Marquardt backpropagation algorithm, a K neighbors regressor (knn), and a passive aggressive regressor (par). These models are based on 14 critical operation parameters that have been provided by downhole sensors during drilling operations such as annular pressure, annular temperature, and rate of penetration, etc. In the study, 4663 data points were selected and included, where 80% to 85% of the data set has been used for training and validation according to the algorithm, and the remaining data points were reserved for testing. In addition, several statistical tests were used to evaluate the accuracy of the models, including root mean square error (RMSE), correlation coefficient (R<sup>2</sup>), and mean squared error (MSE). The results of the developed models show various consistencies and accuracy, while the ANN shows a high accuracy with an R<sup>2</sup> of nearly 0.999 for the training, validation, and testing, as well as the overall of them. The RMSE is 0.000211, 0.000253, 0.00293, and 0.00315 for overall, training, validation, and testing, respectively. This work expands the use of artificial intelligence in the gas and oil industry. The developed ANN model is more flexible in response to challenges, reduces dependence on humans, and thus, reduces the chance of human omission, as well as increasing the efficiency of operations.</p> </abstract>
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7

RASTGOUFARD, P., and R. A. SCHLUETER. "APPLICATION OF CRITICAL MACHINE ENERGY FUNCTION IN POWER SYSTEM TRANSIENT STABILITY ANALYSIS." Electric Machines & Power Systems 16, no. 5 (1989): 343–61. http://dx.doi.org/10.1080/07313568908909392.

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8

Vijayapakavan, P., D. S. Robinson Smart, Kurinjimalar Ramu, and M. Ramachandran. "Superconducting Electromagnetic Launch Machine System for Aerospace Applications." Journal on Applied and Chemical Physics 2, no. 1 (2023): 40–47. http://dx.doi.org/10.46632/jacp/2/1/5.

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The aerospace industry is constantly experimenting with innovative technologies to improve efficiency, effectiveness and sustainability. The use of superconducting machines emerged as a promising solution to address the growing demands of Aerospace applications. Superconducting machines offer significant advantages such as higher power density, reduced weight and improved efficiency compared to conventional electrical machines. However, efficient cooling methods are critical to maintain superconducting materials at low-temperature operating conditions. This abstract provides a comprehensive overview of superconducting machines and their associated cooling systems designed for space applications. A superconducting machine uses high-temperature superconductors to achieve near-zero electrical resistance, enabling high currents to be transmitted with low energy losses. This feature allows development of lightweight and compact electric propulsion systems contribute to improved fuel efficiency and extended mission capabilities in space vehicles. A cooling system is an important component of a superconducting machine because it ensures that the superconducting materials remain below their critical temperature. Various cooling techniques are being explored, including cryogenic cooling, liquid nitrogen cooling, and cryocoolers. These cooling systems effectively extract the heat generated during engine operation, maintaining the superconducting components in their superconducting state.
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9

Cristina Castejon, Cristina, Marıa Jesus Gomez, Juan Carlos Garcia-Prada, and Eduardo Corral. "Energy Distribution Analysis Regarding the Crack Size in a Rotating Shaft." Volume 24, No 3, September 2019 24, no. 3 (2019): 418–25. http://dx.doi.org/10.20855/ijav.2019.24.31190.

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Maintenance is critical to avoid catastrophic failures in rotating machinery, and the detection of cracks plays a critical role because they can originate failures with costly processes of reparation, especially in shafts. Vibration signals are widely used in machine monitoring and fault diagnostics. The most critical issue in machine monitoring is the suitable selection of the vibration parameters that represent the condition of the machine. Discrete Wavelet Transform, and one of its recursive forms, called Wavelet Packet Transform, provide a high potential for pattern extraction. Several factors must be selected and taken into account in the Wavelet Transform application such as the level of decomposition, the suitable mother wavelet, and the level basis or features. In this work, the dynamic response of a shaft with different levels of crack is studied. The evolution of energy of the vibration signals obtained from the rotating shaft and the frequencies where maximum increments of energy appear with the crack are analyzed. The results allow the conclusion that changes in energies computed by means of the Wavelet Packet Transform can be successfully used for crack detection.
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10

Trontl, Krešimir, Dubravko Pevec, and Tomislav Šmuc. "Machine Learning of the Reactor Core Loading Pattern Critical Parameters." Science and Technology of Nuclear Installations 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/695153.

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The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm, and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. We employ a recently introduced machine learning technique, support vector regression (SVR), which is a data driven, kernel based, nonlinear modeling paradigm, in which model parameters are automatically determined by solving a quadratic optimization problem. The main objective of the work reported in this paper was to evaluate the possibility of applying SVR method for reactor core loading pattern modeling. We illustrate the performance of the solution and discuss its applicability, that is, complexity, speed, and accuracy.
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