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Journal articles on the topic 'Power processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Moraes, Carlos H. V. de, Maurilio P. Coutinho, Germano Lambert-Torres, and Luiz Eduardo Borges da Silva. "Real Intelligent Alarm Processing Implementations in Power Control Centers." International Journal of Computer and Electrical Engineering 6, no. 2 (2014): 95–100. http://dx.doi.org/10.7763/ijcee.2014.v6.801.

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2

TAKAHASHI, Ryo, Shun-ichi AZUMA, Mikio HASEGAWA, Hiroyasu ANDO, and Takashi HIKIHARA. "Power Processing for Advanced Power Distribution and Control." IEICE Transactions on Communications E100.B, no. 6 (2017): 941–47. http://dx.doi.org/10.1587/transcom.2016ebn0005.

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3

Scheidegger, R., W. Santiago, K. E. Bozak, L. R. Pinero, and A. Birchenough. "(Invited) High Power SiC Power Processing Unit Development." ECS Transactions 69, no. 11 (October 2, 2015): 13–19. http://dx.doi.org/10.1149/06911.0013ecst.

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4

Alarcon-Rojo, A. D., H. Janacua, J. C. Rodriguez, L. Paniwnyk, and T. J. Mason. "Power ultrasound in meat processing." Meat Science 107 (September 2015): 86–93. http://dx.doi.org/10.1016/j.meatsci.2015.04.015.

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5

Matsumoto, Yuki, Tomoya Tanaka, Koji Sonoda, Kensuke Kanda, Takayuki Fujita, and Kazusuke Maenaka. "Low Power ECG Processing ASIC." IEEJ Transactions on Sensors and Micromachines 134, no. 5 (2014): 108–13. http://dx.doi.org/10.1541/ieejsmas.134.108.

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6

Lei Wang and N. R. Shanbhag. "Low-power MIMO signal processing." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 11, no. 3 (June 2003): 434–45. http://dx.doi.org/10.1109/tvlsi.2003.812367.

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7

Marshall, Larry, and Gabriel Kra. "The Processing Power of Light." Optics and Photonics News 13, no. 3 (March 1, 2002): 38. http://dx.doi.org/10.1364/opn.13.3.000038.

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8

MATSUMOTO, YUKI, TOMOYA TANAKA, KOJI SONODA, KENSUKE KANDA, TAKAYUKI FUJITA, and KAZUSUKE MAENAKA. "Low-Power ECG Processing ASIC." Electronics and Communications in Japan 99, no. 4 (March 16, 2016): 13–20. http://dx.doi.org/10.1002/ecj.11778.

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9

Schmid Mast, Marianne, Mahshid Khademi, and Tristan Palese. "Power and social information processing." Current Opinion in Psychology 33 (June 2020): 42–46. http://dx.doi.org/10.1016/j.copsyc.2019.06.017.

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10

SPIRIDONOV, VALERY. "COHERENT SIGNALS PROCESSING BY ANALOG LOGICAL ELEMENTS." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 02 (March 2007): 333–50. http://dx.doi.org/10.1142/s0219691307001781.

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The result of action of the matched linear filters that are optimal for detection of given signals is maximization of the signal-to-noise ratio [Formula: see text], where PS max is the peak power of the signal, and [Formula: see text] is the average power of a noise. However, maximization of SNR, as a fraction, is obtained formally by the ratio [Formula: see text], where [Formula: see text] is the average power of the signal. PN min is the minimum, in a certain sense, the power of the noise. When n coherent pulses are processed, it is proposed to suppose that PN min is the minimum instantaneous power of available noise powers. In this case, the processing scheme represents the selector that constantly chooses the voltage with the minimum noise instantaneous power and transmits it to output. As shown in the paper, it is possible to perform this selection with acceptable accuracy and to improve SNR and, hence, probabilistic characteristics of detection in comparison with addition of coherent pulses. This improvement is obtained by using the adder input noises when their instantaneous powers are less than the instantaneous power of the adder output noise.

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11

Raju, Jadi, MD Shabazkhan, and G. Laxmi Narayana. "Input Based Dynamic Reconfiguration for Low Power Image Processing and Secure Transmission." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 904–12. http://dx.doi.org/10.31142/ijtsrd7043.

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12

Mueller, Conrad S. M. "A New Computational Model for Real Gains in Big Data Processing Power." Advances in Cyber-Physical Systems 2, no. 1 (March 28, 2017): 11–21. http://dx.doi.org/10.23939/acps2017.01.011.

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13

Gibson, Jerry. "Entropy Power, Autoregressive Models, and Mutual Information." Entropy 20, no. 10 (September 30, 2018): 750. http://dx.doi.org/10.3390/e20100750.

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Autoregressive processes play a major role in speech processing (linear prediction), seismic signal processing, biological signal processing, and many other applications. We consider the quantity defined by Shannon in 1948, the entropy rate power, and show that the log ratio of entropy powers equals the difference in the differential entropy of the two processes. Furthermore, we use the log ratio of entropy powers to analyze the change in mutual information as the model order is increased for autoregressive processes. We examine when we can substitute the minimum mean squared prediction error for the entropy power in the log ratio of entropy powers, thus greatly simplifying the calculations to obtain the differential entropy and the change in mutual information and therefore increasing the utility of the approach. Applications to speech processing and coding are given and potential applications to seismic signal processing, EEG classification, and ECG classification are described.

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14

Svietkina, O. Yu, A. I. Tarasova, O. B. Netiaga, and P. O. Yegorov. "Processing of waste heat power industry." Mining of Mineral Deposits 9, no. 4 (December 30, 2015): 453–60. http://dx.doi.org/10.15407/mining09.04.453.

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15

Gu, Yuan Yuan, Guo Xing Wu, Hui Lu, Yan Cui, and Hao Wang. "High Power Diode Laser for Processing." Advanced Materials Research 472-475 (February 2012): 2508–13. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.2508.

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As diode laser is simple in structure, small size, longer life expectancy and easy modulation and the advantages of low prices, widely used in the industry processing, such as heat treating, welding, hardening, cladding and so on. Respectively, diode laser could make it possible to establish the practical application because of rectangular beam patterns which are suitable to make fine bead with less power. Therefore diode laser cladding will open a new field of repairing for the damaged machinery parts which must contribute to recycling of the used machines and saving of cost. in order to output high power, in this paper, we utilized polarization coupling technology to couple two 808nm laser diode stack together, and designed the optical system to expand and focus the beam, through the experiment, we realized the overall efficiency more than 60%, focusing the beam size of 2×2mm2.

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16

Shanmugarajan, B., and G. Buvanashekaran. "Power Beam Processing of Stainless Steels." Advanced Materials Research 794 (September 2013): 332–39. http://dx.doi.org/10.4028/www.scientific.net/amr.794.332.

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Stainless steels are one of the versatile materials available in five grades viz., austenitic, ferritic, martensitic, duplex stainless steels and precipitation hardenable variety, having applications in various industrial sectors covering thermal power plants, nuclear, fertilizer, urea processing plants, cryogenic industries, aerospace & defence, etc. Each grade of stainless steels has its own unique characteristics in terms of strength, corrosion resistance, hardening behavior etc. Power beam processing using lasers or electron beam can be effectively utilized to process almost all the grades of stainless steels to enhance the performance for intended application. The non-contact and autogenous nature of the process coupled with precise and low heat input processing offers greater benefits compared to processing with conventional processes. This paper describes the application and advantages of power beam processing of different grades of stainless steels. Keywords: laser processing, electron beam processing, stainless steels, welding, cladding, hardening.

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17

Bickford, James A., and Paul Ward. "Low-power signal processing using MEMS." Journal of the Acoustical Society of America 122, no. 5 (2007): 2509. http://dx.doi.org/10.1121/1.2801821.

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18

Singer, S. "Gyrators Application in Power Processing Circuits." IEEE Transactions on Industrial Electronics IE-34, no. 3 (August 1987): 313–18. http://dx.doi.org/10.1109/tie.1987.350978.

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19

Hu, Jianli, Yong Wang, Dave VanderWiel, Cathy Chin, Daniel Palo, Robert Rozmiarek, Robert Dagle, James Cao, Jamie Holladay, and Ed Baker. "Fuel processing for portable power applications." Chemical Engineering Journal 93, no. 1 (May 2003): 55–60. http://dx.doi.org/10.1016/s1385-8947(02)00108-0.

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20

Shenoy, Pradeep S., and Philip T. Krein. "Differential Power Processing for DC Systems." IEEE Transactions on Power Electronics 28, no. 4 (April 2013): 1795–806. http://dx.doi.org/10.1109/tpel.2012.2214402.

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21

Kirschen, D. S., and B. F. Wollenberg. "Intelligent alarm processing in power systems." Proceedings of the IEEE 80, no. 5 (May 1992): 663–72. http://dx.doi.org/10.1109/5.137221.

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22

Celma, A. R., S. Rojas, and F. López-Rodríguez. "Industrial sludge processing for power purposes." Applied Thermal Engineering 28, no. 7 (May 2008): 745–53. http://dx.doi.org/10.1016/j.applthermaleng.2007.06.015.

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23

Bădulescu, Camelia. "Solubilization Processing of Ashes Power Plant." Mining Revue 27, no. 1 (March 1, 2021): 45–51. http://dx.doi.org/10.2478/minrv-2021-0006.

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Abstract The paper presents the attempts of acid and alkaline solubilization of the power plant ashes, using different chemical reagents, at different concentrations. Due to the mineralogical content of these ashes, was elaborated a technological flow for the recovery the iron minerals, resulting an ferrous concentrate containing noble metals. For their recovery from ferrous concentrate, were studied properties of gold, silver and platinum, the conclusion being that these chemical elements can be extracted, in economic conditions, with sodium hypochlorite.

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24

Feng, H., W. Yang, and T. Hielscher. "Power Ultrasound." Food Science and Technology International 14, no. 5 (October 2008): 433–36. http://dx.doi.org/10.1177/1082013208098814.

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The use of ultrasound as a processing aid has been explored in various industrial sectors for over fifty years, but the application of this physical energy in food processing is relatively new. In this article, the current research and development activities of power ultrasound centered on food and bioprocessing applications are summarized. The mode of action of ultrasonication is attributed to several mechanical and chemical actions caused by cavitation, which include localized hot spots, formation of shock waves, microsteaming, and liquid jets, as well as the production of chemical species that will also impact the process kinetics and food quality attributes. Means to enhance cavitation activity is addressed. The research needs for the future development of ultrasound food processing are also discussed.

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25

Kaefer, G., J. Haid, K. Voit, and R. Weiss. "Architectural Software Power Estimation Support for Power Aware Remote Processing." International Journal of Computers and Applications 26, no. 2 (January 2004): 1–6. http://dx.doi.org/10.1080/1206212x.2004.11441733.

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26

SRINIVASAN, RAMESH, and RAMESH ORUGANTI. "Single-phase parallel power processing scheme with power factor control." International Journal of Electronics 80, no. 2 (February 1996): 291–306. http://dx.doi.org/10.1080/002072196137471.

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27

Schaef, Christopher, and Jason T. Stauth. "Multilevel Power Point Tracking for Partial Power Processing Photovoltaic Converters." IEEE Journal of Emerging and Selected Topics in Power Electronics 2, no. 4 (December 2014): 859–69. http://dx.doi.org/10.1109/jestpe.2014.2332952.

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28

Zientarski, Jonatan Rafael Rakoski, Mario Lucio da Silva Martins, Jose Renes Pinheiro, and Helio Leaes Hey. "Evaluation of Power Processing in Series-Connected Partial-Power Converters." IEEE Journal of Emerging and Selected Topics in Power Electronics 7, no. 1 (March 2019): 343–52. http://dx.doi.org/10.1109/jestpe.2018.2869370.

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29

Fukuyama, Yoshikazu, and Yosuke Nakanishi. "Fast power flow for radial power systems using parallel processing." Electrical Engineering in Japan 117, no. 1 (1996): 11–18. http://dx.doi.org/10.1002/eej.4391170102.

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30

Haiying Xu, Haiying Xu, Zhifei Miao Zhifei Miao, Zhigang Che Zhigang Che, Wei Zhang Wei Zhang, Shikun Zou Shikun Zou, and Ziwen Cao Ziwen Cao. "Inverter power supply and control system for high pulse energy laser shock processing." Chinese Optics Letters 10, s2 (2012): S21418–321422. http://dx.doi.org/10.3788/col201210.s21418.

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31

Morimoto, Yasutomi, Junya Okazaki, Shigeru Mihara, Mikio Shimojo, Tadashi Sasaki, Mamoru Numata, Mitsushi Motoyama, et al. "ICONE19-43160 Development of Spent Ion Exchange Resin Processing in Nuclear Power Stations." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_56.

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32

Wang, Song Bai, Chang Ming Cheng, Wei Lan, Ren Wu Zhou, Xian Hui Zhang, Dong Ping Liu, and Size Yang. "Energy Loss on High-Temperature Plasma Processing Waste Printed Circuit Boards." Applied Mechanics and Materials 423-426 (September 2013): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.904.

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This paper estimates the electrical energy loss on high-temperature plasma processing waste printed circuit boards. Using three plasma torch powers in big output power (60W) and a plasma torch power in relatively small output power (30W), after 4.5 hours of high-temperature plasma incineration, 43 kilograms of waste printed circuit boards was successively fused at high temperature plasma incinerator. After calculating, the total electrical energy loss for four powers was about 1,070 kilowatts hours during sample incineration.

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33

ABE, Nobuyuki. "Materials Processing by High Power and High Power Density Diode Laser." Review of Laser Engineering 28, no. 1 (2000): 34–39. http://dx.doi.org/10.2184/lsj.28.34.

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34

Grandinetti, L., and D. Conforti. "Optimization of Power Flow in Electric Power Systems via Parallel Processing." IFAC Proceedings Volumes 20, no. 9 (August 1987): 739–43. http://dx.doi.org/10.1016/s1474-6670(17)55797-3.

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35

Lo, D. S., C. P. Henze, and J. H. Mulkern. "A compact DC-to-DC power converter for distributed power processing." IEEE Transactions on Power Electronics 7, no. 4 (October 1992): 714–24. http://dx.doi.org/10.1109/63.163639.

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36

Jeon, Young-Tae, Hyunji Lee, Katherine A. Kim, and Joung-Hu Park. "Least Power Point Tracking Method for Photovoltaic Differential Power Processing Systems." IEEE Transactions on Power Electronics 32, no. 3 (March 2017): 1941–51. http://dx.doi.org/10.1109/tpel.2016.2556746.

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37

Wu, Cheng-Yen, Hsin-Chiang You, Ching-cheng Liu, and Wen-Luh Yang. "Analysis and Processing of Power Output Signal of 200V Power Devices." Information Engineering 4 (2015): 23. http://dx.doi.org/10.14355/ie.2015.03.005.

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38

Li, Huan, Sinan Li, and Weidong Xiao. "Single-Phase LED Driver With Reduced Power Processing and Power Decoupling." IEEE Transactions on Power Electronics 36, no. 4 (April 2021): 4540–48. http://dx.doi.org/10.1109/tpel.2020.3030052.

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39

Scholl, Annika, Johannes Bloechle, Kai Sassenberg, Stefan Huber, and Korbinian Moeller. "The power to adapt: How sense of power predicts number processing." Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale 73, no. 3 (September 2019): 157–66. http://dx.doi.org/10.1037/cep0000166.

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40

Ando, Hiroyasu, and Shun-ichi Azuma. "Special issue on power distribution and processing." Nonlinear Theory and Its Applications, IEICE 9, no. 3 (2018): 305. http://dx.doi.org/10.1587/nolta.9.305.

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41

TSUBOTA, Shuho, and Takashi ISHIDE. "Application of Laser Processing to Power Plants." Journal of The Institute of Electrical Engineers of Japan 131, no. 1 (2011): 21–23. http://dx.doi.org/10.1541/ieejjournal.131.21.

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42

ABE, Nobuyuki. "Materials Processing with High Power Diode Lasers." Journal of the Japan Welding Society 72, no. 8 (2003): 606–9. http://dx.doi.org/10.2207/qjjws1943.72.606.

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43

Ahmed, Shabbir, Romesh Kumar, and Michael Krumpelt. "Fuel processing for fuel cell power systems." Fuel Cells Bulletin 2, no. 12 (September 1999): 4–7. http://dx.doi.org/10.1016/s1464-2859(00)80122-4.

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44

Masselos, K., P. Merakos, T. Stouraitis, and C. E. Goutis. "Low power architectures for digital signal processing." Journal of Systems Architecture 46, no. 7 (April 2000): 551–71. http://dx.doi.org/10.1016/s1383-7621(99)00018-1.

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45

McCorriston, S., C. W. Morgan, and A. J. Rayner. "Processing Technology, Market Power and Price Transmission." Journal of Agricultural Economics 49, no. 2 (June 1998): 185–201. http://dx.doi.org/10.1111/j.1477-9552.1998.tb01263.x.

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46

Manteca, Paula, and Mariano Martín. "Integrated Facility for Power Plant Waste Processing." Industrial & Engineering Chemistry Research 58, no. 15 (October 24, 2018): 6155–62. http://dx.doi.org/10.1021/acs.iecr.8b04029.

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47

Frantz, Gene, Jorg Henkel, Jan Rabaey, Todd Schneider, Marilyn Wolf, and Umit Batur. "Ultra-Low Power Signal Processing [DSP Forum." IEEE Signal Processing Magazine 27, no. 2 (March 2010): 149–54. http://dx.doi.org/10.1109/msp.2009.935417.

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48

Ludwig, J. T., S. H. Nawab, and A. P. Chandrakasan. "Low-power digital filtering using approximate processing." IEEE Journal of Solid-State Circuits 31, no. 3 (March 1996): 395–400. http://dx.doi.org/10.1109/4.494201.

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49

Nakamura, Takashi, and Constance L. Senior. "Solar Thermal Power for Lunar Materials Processing." Journal of Aerospace Engineering 21, no. 2 (April 2008): 91–101. http://dx.doi.org/10.1061/(asce)0893-1321(2008)21:2(91).

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50

Vollmar, Melanie, James M. Parkhurst, Dominic Jaques, Arnaud Baslé, Garib N. Murshudov, David G. Waterman, and Gwyndaf Evans. "The predictive power of data-processing statistics." IUCrJ 7, no. 2 (February 27, 2020): 342–54. http://dx.doi.org/10.1107/s2052252520000895.

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This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant.

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