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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 (January 29, 2020): 1903242. http://dx.doi.org/10.1002/aenm.201903242.
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Ohtani, Hisashi. "Development of Energy-Saving Machine Tool." International Journal of Automation Technology 11, no. 4 (June 29, 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 (June 29, 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 (June 17, 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 (January 20, 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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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 (January 1989): 343–61. http://dx.doi.org/10.1080/07313568908909392.
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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 (June 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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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 (September 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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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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Albdery, Mohsin Hassan, and István Szabó. "A Recent Machine Learning Techniques for Failure Diagnosis of Rolling Element Bearing." Hungarian Agricultural Engineering, no. 39 (2021): 42–53. http://dx.doi.org/10.17676/hae.2021.39.42.
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Rolling element bearings are critical components of rotating machines, and fault in the bearing can cause the machine to fail. Bearing failure is one of the leading causes of failure in various rotating machines used in industry at high and low speeds. Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is an essential element in the diagnosis of defects that saves time and expenses and avoids dangerous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article gives a short overview of recent trends in the use of machine learning for fault detection. Finally, Deep Learning techniques were recently developed to monitor the health of the intelligent machine are discussed.
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Neugebauer, Reimund, Carsten Hochmuth, Gerhard Schmidt, and Martin Dix. "Energy Efficient Process Planning Based on Numerical Simulations." Advanced Materials Research 223 (April 2011): 212–21. http://dx.doi.org/10.4028/www.scientific.net/amr.223.212.
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The main goal of energy-efficient manufacturing is to generate products with maximum value-added at minimum energy consumption. To this end, in metal cutting processes, it is necessary to reduce the specific cutting energy while, at the same time, precision requirements have to be ensured. Precision is critical in metal cutting processes because they often constitute the final stages of metalworking chains. This paper presents a method for the planning of energy-efficient machining processes based on numerical simulations. It encompasses two levels of planning flexibility: process adjustment and process design. At the process adjustment level, within the constraints of existing machines and tools, numerical simulations of orthogonal cutting are used to determine cutting parameters for increased energy efficiency. In this case, the model encompasses specific cutting energy, tool wear, chip geometry, and burr shape. These factors determine the energy and resources required for the chip formation itself, tool replacements, cleaning and deburring and with that the overall energy efficiency and precision. In the context of process design, with the ability to select machines, machine configurations, tools, and cooling systems, numerical simulations of cutting processes that incorporate machine and tool conditions are applied in the planning of energy-efficient machining. The method is demonstrated for the case of drilling processes and supported by experimental investigations that identify the main influences on energy efficiency in drilling.
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Vyas, Nisarg, Jonathan Farringdon, David Andre, and John Ivo Stivoric. "Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure." AI Magazine 33, no. 2 (March 16, 2012): 55. http://dx.doi.org/10.1609/aimag.v33i2.2408.
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In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.
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Rumaherang, Wulfilla M., J. Louhenapessy, Mesak F. Noya, and Cendy S. Tupamahu. "STUDI EKSPERIMENTAL PERFORMANCE KAVITASI WATERJET PROPULSI." ALE Proceeding 4 (August 17, 2021): 112–20. http://dx.doi.org/10.30598/ale.4.2021.112-120.
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Cavitation is a complex phenomenon of dynamic processes in hydraulic machines that can cause a decrease in energy performance, vibration and damage the blade surfaces. Analysis of cavitation symptoms in hydraulic machines is carried out through cavitation performance studies, namely the relations between energy parameters. Each hydraulic machine has a critical value on a different cavitation performance curve. Therefore, a study of the effect of cavitation changes is needed to determine the working zone of hydraulic machines without cavitation. In this study, cavitation performance analysis was carried out on a waterjet propulsor model with 5 impeller blades and 7 stator blades using experimental methods. The cavitation coefficient was varied at σ = 2.25 to 0.25 by setting and controlling the inlet pressure on the cavitation test rig. The critical point value will be observed at the point where the thrust coefficient decreased to 3.28%. The results showed that cavitation begins at σ = 1, the critical point is obtained at σ = 0.75. From these studies, we find that waterjet must be operated at conditions where is σ > 0.75.
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Díaz, Pedro-J., Jenny-M. Carvajal, and Miguel-Fernando Palencia-Muñoz. "Double torsion testing machine to determine the subcritical fracture index in rocks." CT&F - Ciencia, Tecnología y Futuro 4, no. 3 (May 24, 2011): 37–46. http://dx.doi.org/10.29047/01225383.237.
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This paper discusses the design methodology applied to build a testing machine to determine the sub-critical fracture index in a rock, based on double torsion testing in order to characterize naturally fractured formations such as those located in the Colombian Llanos Foothill Basin . These formations have been subjected to cyclic loads over time, causing fractures that trend to spread at sub-critical stress intensity values. Similarly, it presents the results of testing conducted on nine specimens of the Tambor Formation from 2 different outcrops to establish the testing traceability in the equipment.
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Vyas, Nisarg, Jonathan Farringdon, David Andre, and John Stivoric. "Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 2 (August 11, 2011): 1613–20. http://dx.doi.org/10.1609/aaai.v25i2.18848.
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In this paper we provide insight into the BodyMedia FIT® armband system— a wearable multi-sensor technology that achieves the goals of continuous physiological monitoring (especially energy expenditure estimation) and weight management using machine learning and data modeling methods. This system has been commercially available since 2001 and more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.
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Graffeo, Federica, Silvio Vaschetto, Alessio Miotto, Fabio Carbone, Alberto Tenconi, and Andrea Cavagnino. "Lumped-Parameters Thermal Network of PM Synchronous Machines for Automotive Brake-by-Wire Systems." Energies 14, no. 18 (September 8, 2021): 5652. http://dx.doi.org/10.3390/en14185652.
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Thermal analysis represents a key factor in electrical machine design due to the impact of temperature increase on insulation lifetime. In this context, there has been a wide investigation on thermal modeling, particularly for machines used in harsh working conditions. In this perspective, brake-by-wire (BBW) systems represent one of the most challenging applications for electrical machines used for automotive smart actuators. Indeed, electro-actuated braking systems are required to repeatedly operate the electric machine in high overload conditions in order to limit the actuator response time, as well as to enhance gravimetric and volumetric specific performance indexes. Moreover, BBW systems often impose unconventional supply conditions to the electric machine, consisting of dc currents in three-phase windings to keep the rotor fixed during the braking intervals. However, a dc supply leads to uneven temperature distributions in the machine, and simplified thermal models may not accurately represent the temperature variations for the different machine parts. Considering such unconventional supply conditions, this paper initially investigates the applicability of a conventional lumped-parameters thermal network (LPTN) based on symmetry assumptions for the heat paths and suitable for surface-mounted PM synchronous machines used in BBW systems. An extensive test campaign consisting of pulses and load cycle tests representative of the real machine operations was conducted on a prototype equipped with several temperature sensors. The comparison between measurements and predicted average temperatures, together with insights on the unbalanced heat distribution under the dc supply obtained by means of finite element analyses (FEA), paved the way for the proposal of a phase-split LPTN with optimized parameters. The paper also includes a critical analysis of the optimized parameters, proposing a simplified, phase-split lumped-parameters thermal model suitable to predict the temperature variations in the different machine parts for PM synchronous electric machines used in BBW systems.
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Puspita Sari, Talitha, Rafin Aqsa Izza Mahendra, Ardyono Priyadi, Vita Lystianingrum, Margo Pujiantara, and Sjamsjul Anam. "Perbaikan CCT Pada Multi Machine Infinite Bus Dengan Supercapacitor Energy Storage Menggunakan Critical Trajectory." Jurnal FORTECH 1, no. 2 (August 24, 2020): 61–67. http://dx.doi.org/10.32492/fortech.v1i2.225.
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Transient stability is an important aspect in maintaining the continuity and reliability of the electrical power system when a sudden large disturbance occurs. However, there is a time operation's limitation of the protection system to eliminate disturbance before the system becomes unstable and loses its synchronization. Critical Clearing Time (CCT) is the toleration time to eliminate the fault to keep the system stable. To improve system stability, installing Supercapacitor Energy Storage (SCES) can be one of the methods for extending the CCT values. SCES absorb large amounts of electricity simultaneously when a fault occurs. This paper proposed a determination of SCES' controller effect in extending the CCT. SCES becomes a dummy load and extends the CCT value depending on the controller's ability to respond to a fault. Controller modeling is applied to SCES, which is installed at the bus generator with determined capacity. The modified Fouad and Anderson 9-bus 3-machine system with a single machine to an infinite bus is used to validate the proposed method. Moreover, the critical trajectory method, which is known as a faster calculation and better accuracy than the time domain simulation method, is used to obtain the CCT value. The result shows that the faster controllers work against fault, the higher the CCT value improvement of system CCT. The highest improvement occurs when the controller works at 0.001s.
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STEIF, ALAN R. "MULTIPARTICLE SOLUTIONS IN 2+1 GRAVITY AND TIME MACHINES." International Journal of Modern Physics D 03, no. 01 (March 1994): 277–80. http://dx.doi.org/10.1142/s0218271894000459.
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Multiparticle solutions for sources moving at the speed of light and corresponding to superpositions of single-particle plane-wave solutions are constructed in 2+1 gravity. It is shown that the two-particle spacetimes admit closed timelike curves provided the center-of-momentum energy exceeds a certain critical value. This occurs, however, at the cost of unphysical boundary conditions which are analogous to those affecting Gott’s time machine. As the energy exceeds the critical value, the closed timelike curves first occur at spatial infinity, then migrate inward as the energy is further increased. The total mass of the system also becomes imaginary for particle energies greater than the critical value.
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Park, Hae Min, Jong Hyuk Lee, and Kyung Doo Kim. "Wall temperature prediction at critical heat flux using a machine learning model." Annals of Nuclear Energy 141 (June 2020): 107334. http://dx.doi.org/10.1016/j.anucene.2020.107334.
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Asif, Muhammad, Hang Shen, Chunlin Zhou, Yuandong Guo, Yibo Yuan, Pu Shao, Lan Xie, and Muhammad Shoaib Bhutta. "Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions." Sustainability 15, no. 10 (May 19, 2023): 8298. http://dx.doi.org/10.3390/su15108298.
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Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.
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White, G., S. Gessner, E. Adli, G. J. Cao, K. Sjobak, S. Barber, C. Schroeder, et al. "Beam delivery and final focus systems for multi-TeV advanced linear colliders." Journal of Instrumentation 17, no. 05 (May 1, 2022): P05042. http://dx.doi.org/10.1088/1748-0221/17/05/p05042.
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Abstract The Beam Delivery System (BDS) is a critical component of a high-energy linear collider. It transports the beam from the accelerator and brings it to a focus at the Interaction Point. The BDS system includes diagnostic sections for measuring the beam energy, emittance, and polarization, as well as collimators for machine protection. The length of the BDS increases with collision energy. Higher collision energies also require higher luminosities, and this is a significant constraint on the design for energy-frontier machines. Here, we review BDS designs based on traditional quadrupole magnets and examine the challenges involved in extending these to the Multi-TeV regime consistent with requirements for advanced accelerator concepts.
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Rawat, Nishant. "Water Quality Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 4173–87. http://dx.doi.org/10.22214/ijraset.2022.44658.
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Abstract: Freshwater is a critical resource for agriculture and industry's survival. Examination of water quality is a fundamental stage in the administration of freshwater assets. As indicated by the World Health Organization's yearly report, many individuals are getting sick or some are dead due to the lack of safe drinking water, especially pregnant ladies and kids. It is critical to test the quality of water prior to involving it for any reason, whether it is for animal watering, chemical spraying(Pesticides etc), or drinking water. Water quality testing is a strategy for finding clean drinking water. Accordingly, appropriate water monitoring is basic for safe, clean, and sterile water. Water testing is fundamental for looking at the legitimate working of water sources, testing the safety of drinking water, identifying disease outbreaks, and approving methodology and safeguard activities. Water quality is a proportion of a water’s readiness for a specific utilize in view of physical, chemical, and biological qualities.
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Syukri, Mahdi. "Analysis of using capacitors in 3-phase induction generators to get constant voltage." Jurnal Geuthèë: Penelitian Multidisiplin 6, no. 1 (April 1, 2023): 1. http://dx.doi.org/10.52626/jg.v6i1.208.
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Induction machine is one of the machines that can be applied as an alternative power plant and also as a new renewable energy system. Giving reactive power at the terminal in the form of a capacitor, the induction machine can be used as an induction generator. Reactive power is also needed as a voltage generation process. Because one of the weaknesses of the induction generator is that the voltage generated is very fluctuating when the load being served changes. To overcome this, several capacitors with different values are used. Identify the value of the voltage generated by the induction generator when loading occurs by setting the required capacitor value. Setting the capacitor value is done by using a simulation of the simulink circuit in the MATLAB software. Based on the simulation of loading an induction generator of 6800 watt – 7500 watt different capacitor values are needed starting from 112 uF - 140 uF and to get a constant voltage value.
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Braun, S., P. Schraml, and E. Prof Abele. "Energieverbrauchssimulation von Werkzeugmaschinen*/Process-specific energy simulation of machine tools." wt Werkstattstechnik online 106, no. 03 (2016): 163–68. http://dx.doi.org/10.37544/1436-4980-2016-03-67.
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Energie- und Ressourceneffizienz beschreiben Qualitätsmerkmale, die auch für moderne Werkzeugmaschinen gelten. Der Energieverbrauch von Maschinen bis zu gesamten Fertigungsstandorten muss im Verhältnis zur erzielten Wertschöpfung deutlich gesenkt werden, um wettbewerbsfähig zu bleiben und unserer Verantwortung gegenüber der Umwelt zu entsprechen. Der Fachbeitrag präsentiert anhand eines Fräsprozesses ein modellgestütztes Simulations- und Prognosesystem des Energieverbrauchs von kompletten Bearbeitungsoperationen auf einer Werkzeugmaschine als Basis energetischer Optimierungen. Teil 1 des Fachaufsatzes ist erschienen in der wt-Ausgabe 1/2-2016 auf den Seiten 60 bis 64. Resource efficiency and energy consumption are critical quality attributes of modern machine tools. The energy consumption of machine tools, plants and facilities must be significantly reduced relative to the value added in order to stay competitive and fulfil our responsibility towards the environment. This article presents a model-based simulation and prediction system of the expected energy consumption of machine tools executing a given process NC-program as a basis for energetic optimization measures. It is exemplified by milling operations.
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Meng, Fanlin, Kui Weng, Balsam Shallal, Xiangping Chen, and Monjur Mourshed. "Forecasting Algorithms and Optimization Strategies for Building Energy Management & Demand Response." Proceedings 2, no. 15 (August 27, 2018): 1133. http://dx.doi.org/10.3390/proceedings2151133.
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In this paper, we look at the key forecasting algorithms and optimization strategies for the building energy management and demand response management. By conducting a combined and critical review of forecast learning algorithms and optimization models/algorithms, current research gaps and future research directions and potential technical routes are identified. To be more specific, ensemble/hybrid machine learning algorithms and deep machine learning algorithms are promising in solving challenging energy forecasting problems while large-scale and distributed optimization algorithms are the future research directions for energy optimization in the context of smart buildings and smart grids.
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Marcus, Aaron, and Jérémie Jean. "Going green at home: The Green Machine." Information Design Journal 17, no. 3 (December 31, 2009): 235–43. http://dx.doi.org/10.1075/idj.17.3.08mar.
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A global challenge for the 21st century is to find a sustainable way of life. The Green movement has helped to increase people’s awareness of sustainability issues and propelled development of innovative products to help decrease our ecological footprint. Smart Grid applications, which enable users to monitor their household’s energy consumption, are one of these innovative products. Critical data visualization helps to build awareness, but does not result automatically in effecting behavioral changes, which are required to ensure the Earth’s future and survival. The question then shifts to how exactly to motivate, persuade, educate, and lead people to reduce their household energy consumption. Our study proposes to research and analyze different powerful ways to improve “green behavior” by persuading and motivating people to reduce their household’s energy consumption through a mobile phone application we call the “Green Machine.” We have designed and tested a prototype that is based on behavioral change-process issues-analysis to persuade people to “go green.” This article explains the development of the Green Machine user interface, information design, and information visualization.
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Kravets, Svyatoslav, Vladimir Suponyev, and Aleksej Goponov. "Determination of critical depth forces of cutting soils and energy consumption of chain scraper trench excavators." Bulletin of Kharkov National Automobile and Highway University 1, no. 92 (March 4, 2021): 192. http://dx.doi.org/10.30977/bul.2219-5548.2021.92.1.192.
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The presented results of scientific research are aimed at solving the problem, which is associated with an increase in the productivity of the development of trenches for laying engineering communications due to the use of new less energy-intensive soil development processes with the working equipment of chain trench excavators. The aim of the work is to establish the regularities of the interaction of the working equipment of the chain scraper excavator with the soil, in which the cutters work in the critical cutting depth mode. Among the tasks that are directed to achieve the goal, it was necessary to establish the influence of soil development processes in these conditions on the technical parameters of the machine. The methodology for solving the problem is based on the idea of the theory of soil mechanics and the provisions on the critical depth of soil cutting.Applying this knowledge to the operation of the cutters of the chain excavator for cutters guarantees the consumption of the minimum specific energy and obtaining the maximum performance of the machine. Existing studies do not give a complete picture for calculating the parameters of trench excavators in which the cutters of the working body operate in the mode of critical cutting of the soil, which in turn does not allow carrying out a comprehensive calculation of the chain trench excavator and assessing the energy efficiency of its operation. The result of the research is the obtaining of theoretical dependencies for determining the cutting forces of the soil by the working body of the excavator under the conditions of the critical depth of the work of its cutters, as well as dependencies for calculating the energy indicators of the machine. The originality of the solution to the problem lies in an integrated approach, namely, it took into account not only the influence of the work of the cutters in conditions of a critical depth of cut on the technical parameters of the machine, but also the properties of the soils that are being developed. The practical significance of the results of the research is to obtain dependencies that can be used as the basis for the engineering methodology for the integrated calculation of chain-type trench eesquators in which the cutters operate in the critical blocked and half blocked cutting mode of the soil.
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Patil, Prof Sachin Sambhaji, Mahesh Manohar Sirsat, Ajitkumar Vishwakarma Sharma, Aashish Shahi, and Omkar Maruti Halgi. "Web Based Machine Learning Automated Pipeline." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1875–78. http://dx.doi.org/10.22214/ijraset.2023.50406.
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Abstract: With the increasing volume, velocity, veracity, and variety of data, it has become critical to have efficient techniques and tools for managing and analyzing data in machine learning. Abstraction is a powerful concept that allows users to interact with machine learning algorithms without understanding their technical implementation details. In this project the user will provide the dataset in .csv format the dataset is then processed further to different machine learning preprocessing steps like removing unwanted columns, handling missing values, label encoding, outlier detection and removal, normalization, model building, model prediction, and the result can be downloaded as pdf, tracable pdf and CSV, this all processes gives a result of different model and their respective accuracy so that we can choose the best model for that particular dataset. tracable pdf will be containing all the timestamp of the processes done with their respective result, Apart from client-server model user is also provided a api so that all processes can be implemented in different platforms like c++, java, ruby etc. Overall, this paper highlights the critical role of abstraction in managing the complexity of data and machine learning algorithms, enabling more efficient and effective analysis of large and complex datasets.
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Ding, Yakui, Yongping Li, Heran Zheng, Jing Meng, Jing Lv, and Guohe Huang. "Identifying critical energy-water paths and clusters within the urban agglomeration using machine learning algorithm." Energy 250 (July 2022): 123880. http://dx.doi.org/10.1016/j.energy.2022.123880.
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Razzak, Imran, Guandong Xu, and Muhammad Khurram Khan. "Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures." IEEE Transactions on Industrial Informatics 18, no. 5 (May 2022): 3449–51. http://dx.doi.org/10.1109/tii.2021.3128962.
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Peplow, Andrew, Javad Isavand, Afshar Kasaei, Babak Afzali, and Delphine Bard-Hagberg. "A Speed-Variant Balancing Method for Flexible Rotary Machines Based on Acoustic Responses." Sustainability 13, no. 13 (June 28, 2021): 7237. http://dx.doi.org/10.3390/su13137237.
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As rotary machines have become more complicated, balancing processes have been classified as a vital step in condition monitoring to ensure that machines operate reliably, smoothly and safely. All rotating objects will deflect during rotation and all objects possess certain natural frequencies in the absence of rotation. However, an unbalanced object can cause significant unwanted deflection created by resonant vibration at a frequency (cycles/second) close to certain rotational speeds (rotations/second), known as critical speeds. This is especially important for flexible machines which normally work at rotations above their critical speeds. Imbalance is a common problem in flexible rotating machinery that can lead to extreme vibration and noise levels. This is one of the major reasons for studying various balancing methods applied to the vibration response of rotating machines. Recently, the relation between acoustic and vibration response during a rotary machine balancing process based on the original Four-Run method has been presented for constant speed machines. This method cannot be applied to machines in start-up or shut-off. Hence, by considering the acoustic and vibration responses of a machine between its critical speeds, this research presents a new innovative speed-variant balancing method based on the original Four-Run method, named as (PPCS) Peak to Peak for Critical Speeds. The proposed method consists of two major types of application: the first is in the run-up of the machine and the second is in shut down. Experimental laboratory results show that this method can be implemented for speed-variant and flexible rotary machines during run-up or shut-down transient processes based on acoustic and vibration measurements. Further, the results show the same trend in acoustic and vibration responses during balancing process which was shown for constant speed rotary machines. With a 40% improvement in response compared to around 55% obtained by traditional vibration measurements, the results found show an appreciable benefit in an alternative acoustic methodology that may have not been considered previously for run-up and shut-down issues. In addition, since only the magnitude of response is required and this is a non-contact technique an acoustic-only methodology, it can be employed with some confidence as an innovative and readily available method for condition monitoring.
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Jason, Sebagenzi. "Real-time Virtual Machine Energy-Efficient Allocation in Cloud Data Centers Using Interval-packing Methods." Transactions on Machine Learning and Artificial Intelligence 10, no. 6 (December 2, 2022): 15–34. http://dx.doi.org/10.14738/tmlai.106.13419.
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The reduction of power consumption, which can lower the operation costs of Cloud providers, lengthen the useful life of a machine, as well as lessen the environmental effect caused by power consumption, is one of the critical concerns for large-scale Cloud applications. To satisfy the needs of various clients, Virtual Machines (VMs) as resources (Infrastructure as a Service (IaaS)) can be dynamically allocated in cloud data centers. In this research, we study the energy-efficient scheduling of real-time VMs by taking set processing intervals into account, with the providers' goal of lowering power consumption. Finding the best solutions is an NP-complete problem when virtual machines (VMs) share arbitrary amounts of a physical machine's (PM) total capacity, as demonstrated in numerous open-source resources. Our strategy treats the issue as a modified interval partitioning problem and takes into account configurations with dividable capacities to make the problem formulation easier and assist save energy. There are presented both exact and approximate solutions. The proposed systems consume 8–30% less power than the existing algorithms, according to simulation data.
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Priyadi, Ardyono, Nazila Iyyaya Fariha, Talitha Puspita Sari, Vita Lystianingrum, Margo Pujiantara, and Sjamsjul Anam. "Efek Penambahan SCES Pada Sistem Multimesin dengan Damping dan Kontroler Berdasarkan Metode Critical Trajectory." Jurnal FORTECH 1, no. 2 (August 23, 2020): 79–84. http://dx.doi.org/10.32492/fortech.v1i2.228.
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The system can maintain its synchronization under a transient condition. The transient stability system can be evaluated using the critical trajectory method by calculating the Critical Clearing Time (CCT). The advantage of using a critical trajectory method is that the CCT can be found directly and more accurate than the time domain simulation method. This paper proposed the addition of Supercapacitor Energy Storage (SCES) and damping to enhance the transient stability on multi machine system. SCES is an electrical energy storage device that quickly stores and supplies large amounts of electricity, while damper winding damp the oscillations during unstable steady state conditions. Within the addition of SCES and damping in the system, the stability will last longer than before. The stability system is seen by the extended CCT. Furthermore, multi-machine to infinite bus used to validate the proposed method. The test system also includes the Automatic Voltage Regulator (AVR) and the governor
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Chen, Chun-Wei, Chun-Chang Li, and Chen-Yu Lin. "Combine Clustering and Machine Learning for Enhancing the Efficiency of Energy Baseline of Chiller System." Energies 13, no. 17 (August 24, 2020): 4368. http://dx.doi.org/10.3390/en13174368.
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Energy baseline is an important method for measuring the energy-saving benefits of chiller system, and the benefits can be calculated by comparing prediction models and actual results. Currently, machine learning is often adopted as a prediction model for energy baselines. Common models include regression, ensemble learning, and deep learning models. In this study, we first reviewed several machine learning algorithms, which were used to establish prediction models. Then, the concept of clustering to preprocess chiller data was adopted. Data mining, K-means clustering, and gap statistic were used to successfully identify the critical variables to cluster chiller modes. Applying these key variables effectively enhanced the quality of the chiller data, and combining the clustering results and the machine learning model effectively improved the prediction accuracy of the model and the reliability of the energy baselines.
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Salonitis, Konstantinos. "Energy efficiency assessment of grinding strategy." International Journal of Energy Sector Management 9, no. 1 (April 7, 2015): 20–37. http://dx.doi.org/10.1108/ijesm-04-2013-0009.
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Purpose – This paper aims to set the framework for measuring the energy performance of a manufacturing process. The availability and affordability of energy is becoming a critical parameter nowadays, affecting the whole lifecycle of the product, and hence the production phase as well. The energy efficiency of the grinding process, as a widely used manufacturing process in the industry, is assessed with regard to the selected process strategies. Design/methodology/approach – To assess the grinding machine tool energy performance, a measuring framework is designed, implemented and validated. The process strategy effect on the energy consumption is experimentally assessed through energy audits of the grinding machine tool. Such energy audits provide better insights into the way subsystems composing a machine tool affect the energy consumption. Findings – It is revealed that the proper selection of process strategy can significantly reduce the energy consumption. The amount of energy consumed for the actual process is less than the energy required for maintaining the processing environment (e.g. for the coolant pump delivering coolant fluid in the processing area). The key finding is that the measuring framework can be used for the understanding and analysis of the energy consumption of the various machine tool components. Additionally, for the grinding process itself, the energy audits indicate that reducing the processing duration can significantly reduce the overall energy. Originality/value – The main novel contribution of the present paper is the development of a measurement framework for assessing the energy consumption of subsystems running simultaneously when processing a workpiece. Grinding process energy demand is analysed in detail, allowing for the first time to consider energy consumption as a manufacturing decision criterion.
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Kishore, Somasundaram Chandra, Suguna Perumal, Raji Atchudan, Muthulakshmi Alagan, Ashok K. Sundramoorthy, and Yong Rok Lee. "A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis." Catalysts 12, no. 7 (July 5, 2022): 743. http://dx.doi.org/10.3390/catal12070743.
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In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.
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Naik, Ketaki Bhalchandra, G. Meera Gandhi, and S. H. Patil. "Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre." Cybernetics and Information Technologies 18, no. 3 (September 1, 2018): 23–36. http://dx.doi.org/10.2478/cait-2018-0036.
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Abstract Cloud Data centers have adopted virtualization techniques for effective and efficient compilation of an application. The requirements of application from the execution perspective are fulfilled by scaling up and down the Virtual Machines (VMs). The appropriate selection of VMs to handle the unpredictable peak workload without load imbalance is a critical challenge for a cloud data center. In this article, we propose Pareto based Greedy-Non dominated Sorting Genetic Algorithm-II (G-NSGA2) for agile selection of a virtual machine. Our strategy generates Pareto optimal solutions for fair distribution of cloud workloads among the set of virtual machines. True Pareto fronts generate approximate optimal trade off solution for multiple conflicting objectives rather than aggregating all objectives to obtain single trade off solution. The objectives of our study are to minimize the response time, operational cost and energy consumption of the virtual machine. The simulation results evaluate that our hybrid NSGA-II outperforms as compared to the standard NSGA-II Multiobjective optimization problem.
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Yılmaz, Beyza, and Ramazan Yıldırım. "Critical review of machine learning applications in perovskite solar research." Nano Energy 80 (February 2021): 105546. http://dx.doi.org/10.1016/j.nanoen.2020.105546.
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Agarala, Ajaysekhar, Sunil S. Bhat, Arghya Mitra, Daria Zychma, and Pawel Sowa. "Transient Stability Analysis of a Multi-Machine Power System Integrated with Renewables." Energies 15, no. 13 (July 1, 2022): 4824. http://dx.doi.org/10.3390/en15134824.
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The impact on the stability of power systems is rising as the penetration level of renewable energy with sporadic natures rises rapidly on the grid. However, the impact of different types of renewable energy sources (wind, solar) and their combination on system stability varies even with the same penetration level. This paper concentrates mainly on the stability analysis of multi-machine systems connected to various types of renewable energy sources. The study presents a simple and novel control technique named automatic reactive power support (ARS) for both single and combinations of renewable sources by injecting the available reactive power into the system during fault through converters to enhance system stability. The permanent magnet synchronous generator (PMSG) and doubly fed induction generator (DFIG) are both considered as wind generators in this paper for comparison. In addition, transient stability enhancement is carried out by improving critical clearing time of a three-phase fault in the power system. With the creation of a 3-phase fault at various buses, stability analysis is carried out on the 9-bus WSCC test bus system and also on the 68-bus IEEE test system. Comparative analysis of six test case conditions is provided and the considered cases are without renewable source, with DFIG as a wind generator, PMSG as a wind generator, solar PV farm, wind farm with DFIG and solar PV in combination and the combination of wind farm with PMSG and solar PV. Moreover, the improvement in critical clearing time of the system is compared using conventional and proposed controls with all the aforementioned renewable sources. Comparative results show that the proposed control technique improves system stability and also that the combination of renewable energy sources ought to enhance the critical clearing time of system.
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Junaid, Muhammad, Adnan Sohail, Fadi Al Turjman, and Rashid Ali. "Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–35. http://dx.doi.org/10.1145/3433541.
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Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.
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Choi, Gilsu. "Analysis and Experimental Verification of the Demagnetization Vulnerability in Various PM Synchronous Machine Configurations for an EV Application." Energies 14, no. 17 (September 1, 2021): 5447. http://dx.doi.org/10.3390/en14175447.
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Safety is a critical feature for all passenger vehicles, making fail–safe operation of the traction drive system highly important. Increasing demands for traction drives that can operate in challenging environments over wide constant power speed ranges expose permanent magnet (PM) machines to conditions that can cause irreversible demagnetization of rotor magnets. In this paper, a comprehensive analysis of the demagnetization vulnerability in PM machines for an electric vehicle (EV) application is presented. The first half of the paper presents rotor demagnetization characteristics of several different PM machines to investigate the impact of different design configurations on demagnetization and to identify promising machine geometries that have higher demagnetization resistance. Experimental verification results of rotor demagnetization in an interior PM (IPM) machine are presented in the latter half of the paper. The experimental tests were carried out on a specially designed locked-rotor test setup combined with closed-loop magnet temperature control. Experimental results confirm that both local and global demagnetization damage can be accurately predicted by time-stepped finite element (FE) analysis.
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Husainy, Avesahemad S. N., Sairam A. Patil, Atharva S. Sinfal, Vasim M. Mujawar, and Chandrashekhar S. Sinfal. "Parameter Optimization of Refrigeration Chiller by Machine Learning." Asian Journal of Electrical Sciences 12, no. 1 (June 22, 2023): 39–45. http://dx.doi.org/10.51983/ajes-2023.12.1.3684.
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The implementation of machine learning in a chiller system provides several benefits. It can improve energy efficiency by optimizing chiller operation based on predicted load requirements. It can enhance system reliability and reduce maintenance costs by detecting and diagnosing faults in advance. Furthermore, it can enable data-driven decision-making, enabling operators to make informed choices based on accurate predictions and insights. This implementation aims to leverage machine learning techniques to optimize the performance and energy efficiency of a chiller system. Chiller systems are widely used in various industries for cooling purposes, and their efficient operation is critical to reducing energy consumption and operational costs. By employing machine learning algorithms, this implementation aims to analyze historical data, understand patterns, and develop predictive models to optimize chiller system performance. The implementation process involves several steps. First, historical data from the chiller system, including sensor measurements, operating parameters and energy consumption, is collected and preprocessed. The data is then split into training and testing sets. Next, suitable machine learning algorithms, such as regression, classification, or time-series forecasting models, are selected based on the specific goals and requirements of the chiller system. Overall, this implementation demonstrates the potential of machine learning to optimize chiller system performance, reduce energy consumption, and improve operational efficiency. By leveraging historical data and advanced analytics, machine learning can play a crucial role in transforming traditional chiller systems into intelligent, adaptive, and energy-efficient cooling solutions.
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RECHTIN, CYDNEY, CHITTA RANJAN, ANTHONY LEWIS, and BETH ANN ZARKO. "Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning." November 2019 18, no. 11 (December 1, 2019): 679–89. http://dx.doi.org/10.32964/tj18.11.679.
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Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.
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Scafà, Martina, Marco Marconi, and Michele Germani. "A critical review of symbiosis approaches in the context of Industry 4.0☆." Journal of Computational Design and Engineering 7, no. 3 (April 3, 2020): 269–78. http://dx.doi.org/10.1093/jcde/qwaa022.
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Abstract The implementation of symbiosis approaches is recognized as an effective industrial strategy towards the optimization of resource exploitation and the improvement of collaboration in the context of Industry 4.0. An industrial system can be considered as a complex environment in which material, energy, machine, and human resources should cooperate towards the improvement of efficiency and the creation of value. According to this vision, the paper presents a detailed literature review about the existing symbiosis approaches: (i) industrial symbiosis models, which mainly aim at the sharing of resources among different companies, and (ii) human symbiosis, which focuses on how to effectively strengthen the synergy among humans and machines. Strengths, weaknesses and correlations among the most common symbiosis approaches are analysed and classified. Finally, the existing symbiosis models are related with the pillars of the Industry 4.0 paradigm, in order to understand what should be the future directions of research in the context of collaborative manufacturing.
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AlHaddad, Ulaa, Abdullah Basuhail, Maher Khemakhem, Fathy Elbouraey Eassa, and Kamal Jambi. "Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events." Sustainability 15, no. 16 (August 21, 2023): 12622. http://dx.doi.org/10.3390/su151612622.
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The critical challenge of enhancing the resilience and sustainability of energy management systems has arisen due to historical outages. A potentially effective strategy for addressing outages in energy grids involves preparing for future failures resulting from line vulnerability or grid disruptions. As a result, many researchers have undertaken investigations to develop machine learning-based methodologies for outage forecasting for smart grids. This research paper proposed applying ensemble methods to forecast the conditions of smart grid devices during extreme weather events to enhance the resilience of energy grids. In this study, we evaluate the efficacy of five machine learning algorithms, namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), decision tree (DT), and Naive Bayes (NB), by utilizing the bagging ensemble technique. The results demonstrate a remarkable accuracy rate of 99.98%, with a true positive rate of 99.6% and a false positive rate of 0.01%. This research establishes a foundation for implementing sustainable energy integration into electrical networks by accurately predicting the occurrence of damaged components in the energy grid caused by extreme weather events. Moreover, it enables operators to manage the energy generated effectively and facilitates the achievement of energy production efficiency. Our research contributes to energy management systems using ensemble methods to predict grid vulnerabilities. This advancement lays the foundation for developing resilient and dependable energy infrastructure capable of withstanding unfavorable weather conditions and assisting in achieving energy production efficiency goals.
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Borunda, Monica, Adrián Ramírez, Raul Garduno, Gerardo Ruíz, Sergio Hernandez, and O. A. Jaramillo. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning." Energies 15, no. 23 (November 24, 2022): 8895. http://dx.doi.org/10.3390/en15238895.
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Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P50 and P95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country.
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Chen, James Ming, and Mobeen Ur Rehman. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities." Energies 14, no. 19 (September 24, 2021): 6099. http://dx.doi.org/10.3390/en14196099.
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The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.
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Del Ser, J., D. Casillas-Perez, L. Cornejo-Bueno, L. Prieto-Godino, J. Sanz-Justo, C. Casanova-Mateo, and S. Salcedo-Sanz. "Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives." Applied Soft Computing 118 (March 2022): 108526. http://dx.doi.org/10.1016/j.asoc.2022.108526.
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Forootan, Mohammad Mahdi, Iman Larki, Rahim Zahedi, and Abolfazl Ahmadi. "Machine Learning and Deep Learning in Energy Systems: A Review." Sustainability 14, no. 8 (April 18, 2022): 4832. http://dx.doi.org/10.3390/su14084832.
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With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.