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Journal articles on the topic 'Energy techniques'

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Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

G, Soumya Dath. "Energy Efficient Wireless Sensor Networks: A Survey on Energy-Based Routing Techniques." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 226–31. http://dx.doi.org/10.31142/ijtsrd20304.

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2

Holey, Liz. "Muscle Energy Techniques." Physiotherapy 82, no. 8 (August 1996): 493. http://dx.doi.org/10.1016/s0031-9406(05)66417-6.

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3

Kaur, Diksha, Tek Tjing Lie, Nirmal K. C. Nair, and Brice Vallès. "Wind Speed Forecasting Using Hybrid Wavelet Transform—ARMA Techniques." AIMS Energy 3, no. 1 (2015): 13–24. http://dx.doi.org/10.3934/energy.2015.1.13.

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4

B. Attya, Ayman, and T. Hartkopf. "Wind Turbines Support Techniques during Frequency Drops — Energy Utilization Comparison." AIMS Energy 2, no. 3 (2014): 260–75. http://dx.doi.org/10.3934/energy.2014.3.260.

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5

Khandare, Pooja, Sanjay Deokar, and Arati Dixit. "Relay Coordination and Optimization techniques using DWT-Differentiation Algorithms for Fault Detection in Microgrid." AIMS Energy 8, no. 4 (2020): 563–79. http://dx.doi.org/10.3934/energy.2020.4.563.

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6

Singh, Satendra. "Psychophysiological techniques and energy medicine." International Journal of Yoga 4, no. 1 (2011): 39. http://dx.doi.org/10.4103/0973-6131.78184.

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7

Kumar, A., A. Haberl, H. Bakhru, and B. Rout. "Improved high energy microbeam techniques." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 130, no. 1-4 (July 1997): 219–23. http://dx.doi.org/10.1016/s0168-583x(97)00169-9.

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8

Wang, Hong. "Energy Saving Techniques: An Introduction." Measurement and Control 43, no. 7 (September 2010): 202. http://dx.doi.org/10.1177/002029401004300702.

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9

Whalley, David. "Energy efficient data access techniques." ACM SIGPLAN Notices 49, no. 5 (May 5, 2014): 1. http://dx.doi.org/10.1145/2666357.2602568.

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10

Oyama, T. "Analytic techniques for energy planning." European Journal of Operational Research 20, no. 2 (May 1985): 273–74. http://dx.doi.org/10.1016/0377-2217(85)90074-8.

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11

Saad Bin Arif, M., Uvais Mustafa, and Shahrin bin Md. Ayob. "Extensively used conventional and selected advanced maximum power point tracking techniques for solar photovoltaic applications: An overview." AIMS Energy 8, no. 5 (2020): 935–58. http://dx.doi.org/10.3934/energy.2020.5.935.

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12

M. S. Y. Konara, K., M. L. Kolhe, and Arvind Sharma. "Power dispatching techniques as a finite state machine for a standalone photovoltaic system with a hybrid energy storage." AIMS Energy 8, no. 2 (2020): 214–30. http://dx.doi.org/10.3934/energy.2020.2.214.

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13

Guerra, Gerardo, and Juan A. Martinez-Velasco. "A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing." AIMS Energy 6, no. 5 (2018): 764–800. http://dx.doi.org/10.3934/energy.2018.5.764.

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14

Lisa Xu, Aili Zhang, Ping Liu, Chao Chen, Jianqi Sun, and D. Sabados. "Energy-Based Diagnostic and Treatment Techniques." IEEE Engineering in Medicine and Biology Magazine 27, no. 5 (September 2008): 72–77. http://dx.doi.org/10.1109/memb.2008.923960.

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15

Zhuravlev, Sergey, Juan Carlos Saez, Sergey Blagodurov, Alexandra Fedorova, and Manuel Prieto. "Survey of Energy-Cognizant Scheduling Techniques." IEEE Transactions on Parallel and Distributed Systems 24, no. 7 (July 2013): 1447–64. http://dx.doi.org/10.1109/tpds.2012.20.

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16

Kabius, B., V. Seybold, S. Hiller, A. Rilk, E. Zellmann, and W. Probst. "Energy-Filtering Techniques for Thick Samples." Microscopy and Microanalysis 6, S2 (August 2000): 166–67. http://dx.doi.org/10.1017/s1431927600033328.

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Imaging of sample regions with a thickness significantly larger than the extinction length and strong thickness variations introduces two major problems for transmission electron microscopy (TEM) : (i) inelastic scattering increases the energy width of the transmitted electrons and therefore the resolution decreases (ii) the contrast differences caused by thickness variations can be higher than the dynamic range of the detector system.Both problems can be solved by using energy filtering techniques. The advantage here is that for energy filtered imaging the resolution limit is not determined by the sample thickness but by the width of the energy selection aperture. Fig. 1 shows three envelope functions of the temporal coherence calculated for different values of the energy width. The functions were plotted for an acceleration voltage of 200 kV and a high voltage stability of 2 ppm.

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17

Ferbel, Thomas, and David R. Nygren. "Experimental Techniques in High Energy Physics." Physics Today 41, no. 6 (June 1988): 79–80. http://dx.doi.org/10.1063/1.2811463.

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18

Kaur, Tarandeep, and Inderveer Chana. "Energy Efficiency Techniques in Cloud Computing." ACM Computing Surveys 48, no. 2 (November 21, 2015): 1–46. http://dx.doi.org/10.1145/2742488.

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19

Cardoso, Filipe, Sven Petersson, Mauro Boldi, Shinji Mizuta, Guido Dietl, Rodolfo Torrea-Duran, Claude Desset, Jouko Leinonen, and Luis Correia. "Energy efficient transmission techniques for LTE." IEEE Communications Magazine 51, no. 10 (October 2013): 182–90. http://dx.doi.org/10.1109/mcom.2013.6619582.

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20

Lockwood, Jane. "Muscle Energy Techniques for Muscle Dysfunction." Physiotherapy 84, no. 8 (August 1998): 365. http://dx.doi.org/10.1016/s0031-9406(05)61455-1.

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21

Iida, Yoshihiro. "Future View of Energy Conversion Techniques." Journal of the Society of Mechanical Engineers 95, no. 886 (1992): 774–78. http://dx.doi.org/10.1299/jsmemag.95.886_774.

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22

Al-Homoud, Mohammad Saad. "Computer-aided building energy analysis techniques." Building and Environment 36, no. 4 (May 2001): 421–33. http://dx.doi.org/10.1016/s0360-1323(00)00026-3.

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23

Jagadheeswari, M., and M. Anand kumar. "Energy Efficient Techniques in Wireless Networks." International Journal of Computer Trends and Technology 40, no. 1 (October 25, 2016): 55–59. http://dx.doi.org/10.14445/22312803/ijctt-v40p110.

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24

Sun, Chunhua, and Guangqing Shang. "Multi-Direction Piezoelectric Energy Harvesting Techniques." Journal of Power and Energy Engineering 07, no. 09 (2019): 52–59. http://dx.doi.org/10.4236/jpee.2019.79003.

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25

Ummenhofer, Thomas. "Offshore wind energy - Challenging new techniques." Steel Construction 6, no. 3 (August 2013): 177. http://dx.doi.org/10.1002/stco.201310026.

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26

Takaishi, Masaru. "Energy Saving Techniques for Paper Factory." JAPAN TAPPI JOURNAL 66, no. 7 (2012): 703–10. http://dx.doi.org/10.2524/jtappij.66.703.

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27

Rajagopalan, Priyadarsini, and C. Y. Leung Tony. "Progress on building energy labelling techniques." Advances in Building Energy Research 6, no. 1 (May 2012): 61–80. http://dx.doi.org/10.1080/17512549.2012.672002.

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28

Kolokotsa, Dionysia, Theocharis Tsoutsos, and Sotiris Papantoniou. "Energy conservation techniques for hospital buildings." Advances in Building Energy Research 6, no. 1 (May 2012): 159–72. http://dx.doi.org/10.1080/17512549.2012.672007.

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29

Bowman, Neil T. "Validation of building energy evaluation techniques." International Journal of Ambient Energy 7, no. 3 (July 1986): 145–50. http://dx.doi.org/10.1080/01430750.1986.9675493.

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30

Vatwani, Archana, and Rania Margonis. "Energy Conservation Techniques to Decrease Fatigue." Archives of Physical Medicine and Rehabilitation 100, no. 6 (June 2019): 1193–96. http://dx.doi.org/10.1016/j.apmr.2019.01.005.

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31

Lee, Jae-Wuk, Doohwan Kim, and Jang-Eui Hong. "Code Refactoring Techniques Based on Energy Bad Smells for Reducing Energy Consumption." KIPS Transactions on Software and Data Engineering 5, no. 5 (May 31, 2016): 209–20. http://dx.doi.org/10.3745/ktsde.2016.5.5.209.

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32

Zhang, Qing Liang. "Research on Key Energy Saving Techniques of Automobile Painting." Applied Mechanics and Materials 716-717 (December 2014): 694–97. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.694.

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Based on the domestic automobile body coating technique research, key energy saving techniques in 3 aspects, i.e. painting process, painting equipment and painting management were reviewed respectively. Existing energy conservation problems in Chinese automobile body painting lines were analyzed. The key energy saving of automobile body coating was stated. It was pointed out that energy saving technique of electromechanical system is one of the most potential research direction and development prospect.

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33

Chugh, Amit, and Supriya Panda. "Energy Efficient Techniques in Wireless Sensor Networks." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 13–19. http://dx.doi.org/10.2174/1872212112666180731114046.

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Background: Wireless Sensor Network (WSN) is a collection of tiny electromechanical nodes termed as sensors. Sensors are equipped with sensing unit, which is designed for application specific. When deployed either by planned or unplanned after deployment, sensor’s energy starts depleting due to various roles like sensing, communication and aggregation. Method: WSN is challenged with limited battery power. The aim is to enhance energy efficiency that leads to a prolonged lifetime of networks. Results: We have reviewed the patents related to energy efficiency in wireless sensor networks. This Paper presents the study of various energy efficient techniques, which can enhance the lifetime of sensor networks; it covers basics of WSN, their design, Classification, Communication in WSN and a survey of different techniques for effective utilization of sensor’s energy. Conclusion: Paper has emphasized on energy efficient clustering technique along with feature wise summary of existing clustering protocols.

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34

Wang, Heng, Jiamo Jiang, Jian Li, Manzoor Ahmed, and Mugen Peng. "High Energy Efficient Heterogeneous Networks: Cooperative and Cognitive Techniques." International Journal of Antennas and Propagation 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/231794.

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Heterogeneous network (HetNet) is considered as the main and eminent future communication technology, since it achieves high spectral efficiency per unit area and saves energy due to low transmission power. Mass deployment of small cells in cochannel mode increases overall system capacity, but it is also coupled with greater risk of cochannel interference. This paper overviews the interference model based on the Poisson point process (PPP) and analyzes the performance in terms of energy efficiency in multitier HetNet. As the promising solution for improving the performance of HetNet, both the cooperative communication and cognitive radio techniques to mitigate the interference in HetNet are surveyed. As one example of cooperative communication techniques, a hierarchical cooperation scheme on the spectrum allocation is presented and its energy efficiency performance is analyzed and evaluated. Meanwhile, the energy efficiency increases from the cognitive radio technique are demonstrated as well. The energy efficiency performance comparison between the presented cooperative communication and cognitive radio techniques is emphasized, which suggests that the cooperation communication technique is preferred to suppress the interference and increase the energy efficiency in HetNets.

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35

Chandrasekar, Suraj, Ceylan Colak, Nancy A. Obuchowski, Andrew N. Primak, Wadih Karim, and Naveen Subhas. "Combined Dual-Energy and Single-Energy Metal Artifact Reduction Techniques Versus Single-Energy Techniques Alone for Lesion Detection Near an Arthroplasty." American Journal of Roentgenology 215, no. 2 (August 2020): 425–32. http://dx.doi.org/10.2214/ajr.19.22084.

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36

Gauthier, Lovic, and Tohru Ishihara. "Processor Energy Characterization for Compiler-Assisted Software Energy Reduction." Journal of Electrical and Computer Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/786943.

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Energy consumption is a fundamental barrier in taking full advantage of today and future semiconductor manufacturing technologies. The paper presents our recent research activities and results on characterizing and reducing the energy consumption in embedded systems. Firstly, a technique for characterizing the energy consumption of embedded processors during an application execution is presented. The technique trains a per-processor linear approximation model for fitting it to the energy consumption of the processor obtained by postlayout simulation. Secondly, based on the energy model mentioned above, the paper shows techniques for reducing the energy consumption by optimally mapping program code, stack frames, and data items to the scratch-pad memory (SPM) of the processor memory space.

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37

Narieda, Shusuke, Takeo Fujii, and Kenta Umebayashi. "Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN." Sensors 20, no. 16 (August 7, 2020): 4417. http://dx.doi.org/10.3390/s20164417.

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This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

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38

Sackey, Samson Hansen, Michael Kwame Ansong, Samuel Nartey Kofie, and Abdul Karim Armahy. "Energy Efficient Linear and Non-Linear Precoders for Massive MIMO Systems." International Journal of Computer Networks and Communications Security 8, no. 8 (August 30, 2020): 59–66. http://dx.doi.org/10.47277/ijcncs/8(8)1.

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The term Massive MIMO means, Massive multiple input multiple output also known as (large-scale antenna system, very large MIMO). Massive Multiple-Input-MultipleOutput (MIMO) is the major key technique for the future Fifth Generation (5G) of mobile wireless communication network due to its characteristics, elements and advantages. Massive MIMO will be comprised of five major elements; antennas, electronic components, network architectures, protocols and signal processing. We realize that precoding technique is a processing technique that utilizes Channel State Information Technique (CSIT) by operating on the signals before transmitting them. This technique varies base on the type of CSIT and performance criterion. Precoding technique is the last digital processing block at the transmitting side. In this paper, linear and non-linear Precoding technique was reviewed and we proposed two techniques under each that is Minimum Mean Square Error (MMSE), Block Diagonalization (BD), Tomlinson-Harashima (TH) and Dirty paper coding (DPC). Four Precoding techniques: MMSE, BD, DPC and TH were used in the studies to power consumption, energy efficiency and area throughput for single-cell and multi-cell scenarios. In comparing the proposed techniques, in terms of energy efficiency and area throughput, reuse factor (Reuse 4) performs better than other techniques when there is an imperfect CSI is used

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39

Sun, Chuahua, and Guangqing Shang. "On Broadband Nonlinear Piezoelectric Energy Harvesting Techniques." IOP Conference Series: Materials Science and Engineering 730 (February 11, 2020): 012039. http://dx.doi.org/10.1088/1757-899x/730/1/012039.

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40

Oltjen, J. W. "Integration of Energy Concepts by Modeling Techniques." Journal of Dairy Science 76, no. 6 (June 1993): 1812–16. http://dx.doi.org/10.3168/jds.s0022-0302(93)77513-x.

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41

Kaur, Balinder, and Sunil Nagpal. "Review on Energy Efficient Techniques in MANETs." International Journal of Computer Sciences and Engineering 6, no. 8 (August 31, 2018): 784–89. http://dx.doi.org/10.26438/ijcse/v6i8.784789.

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42

Sharma, Neha, and Neeru Bhardwaj. "Analysis of Energy Efficient Techniques of IoT." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 321–25. http://dx.doi.org/10.26438/ijcse/v7i5.321325.

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43

Canning, T. O. M., F. McNamara, A. Egan, and P. Naughton. "Novel Stored Energy Techniques for Spinning Reserve." IFAC Proceedings Volumes 25, no. 1 (March 1992): 141–46. http://dx.doi.org/10.1016/s1474-6670(17)50443-7.

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44

Wessely, Michelle, Kathleen L. Linaker, and Gilbert Méal. "Chiropractic radiology: case challenge Muscle energy techniques." British Journal of Chiropractic 5, no. 3 (January 2002): 44–45. http://dx.doi.org/10.1016/s1466-2108(02)90016-1.

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45

King, C. R., and T. T. Lee. "Surgical Techniques and Applications of Monopolar Energy." Journal of Minimally Invasive Gynecology 20, no. 6 (November 2013): S84. http://dx.doi.org/10.1016/j.jmig.2013.08.268.

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46

Mujtaba, Ghulam, Muhammad Tahir, and Muhammad Hanif Soomro. "Energy Efficient Data Encryption Techniques in Smartphones." Wireless Personal Communications 106, no. 4 (August 11, 2018): 2023–35. http://dx.doi.org/10.1007/s11277-018-5920-1.

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47

Khatib, Tamer, Azah Mohamed, and K. Sopian. "A review of solar energy modeling techniques." Renewable and Sustainable Energy Reviews 16, no. 5 (June 2012): 2864–69. http://dx.doi.org/10.1016/j.rser.2012.01.064.

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48

Swann, Julie I. "Energy-saving techniques to help manage fatigue." British Journal of Healthcare Assistants 5, no. 11 (November 2011): 528–31. http://dx.doi.org/10.12968/bjha.2011.5.11.528.

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49

Shenkman, A. L. "Energy loss computation by using statistical techniques." IEEE Transactions on Power Delivery 5, no. 1 (1990): 254–58. http://dx.doi.org/10.1109/61.107281.

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50

Malik, S. B., and P. S. Satsangi. "Data extrapolation techniques for energy systems planning." Energy Conversion and Management 38, no. 14 (September 1997): 1459–74. http://dx.doi.org/10.1016/s0196-8904(96)00092-1.

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