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Journal articles on the topic 'Adaptive power management'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Zhiyuan Ren, B. H. Krogh, and R. Marculescu. "Hierarchical Adaptive Dynamic Power Management." IEEE Transactions on Computers 54, no. 4 (April 2005): 409–20. http://dx.doi.org/10.1109/tc.2005.66.

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2

Jiao, Yu, Ali R. Hurson, and Behrooz Shirazi. "Adaptive application-driven WLAN power management." Pervasive and Mobile Computing 3, no. 3 (June 2007): 255–75. http://dx.doi.org/10.1016/j.pmcj.2006.12.003.

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3

Liu, Bo, Yong Liu, Huiyan Zhang, Yonghui Xu, Can Tang, Lianggui Tang, Huafeng Qin, and Chunyan Miao. "Adaptive Power Iteration Clustering." Knowledge-Based Systems 225 (August 2021): 107118. http://dx.doi.org/10.1016/j.knosys.2021.107118.

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4

Wee, Tan Kiat, and Rajesh Krishna Balan. "Adaptive display power management for OLED displays." ACM SIGCOMM Computer Communication Review 42, no. 4 (September 24, 2012): 485–90. http://dx.doi.org/10.1145/2377677.2377770.

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5

Moser, Clemens, Lothar Thiele, Davide Brunelli, and Luca Benini. "Adaptive Power Management for Environmentally Powered Systems." IEEE Transactions on Computers 59, no. 4 (April 2010): 478–91. http://dx.doi.org/10.1109/tc.2009.158.

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6

Zheng, Xinying, and Yu Cai. "CMDP based adaptive power management in server clusters." Sustainable Computing: Informatics and Systems 3, no. 2 (June 2013): 70–79. http://dx.doi.org/10.1016/j.suscom.2012.08.002.

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7

Ro, Cheul-Woo, and Kyung-Min Kim. "Power Management SRN Modeling based on Adaptive Timeout." Journal of the Korea Contents Association 8, no. 1 (January 28, 2008): 300–307. http://dx.doi.org/10.5392/jkca.2008.8.1.300.

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8

Park, S. O., J. K. Lee, J. H. Park, and S. J. Kim. "Adaptive power management system for mobile multimedia device." IET Communications 6, no. 11 (2012): 1407. http://dx.doi.org/10.1049/iet-com.2011.0727.

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9

Levin, Vladimir M., and Ammar A. Yahya. "Adaptive management of technical condition of power transformers." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3862. http://dx.doi.org/10.11591/ijece.v10i4.pp3862-3868.

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Ensuring reliable operation of power transformers as part of electric power facilities is assigned to the maintenance and repair system, whose important components are diagnostics and monitoring of the technical condition. Monitoring allows you to answer the question of whether the transformer abnormalities and how to do they manifest, while diagnostics allow determining the nature, the severity of the problem, determine the cause and possible consequences. The article presents the results of the authors ' research on creating an algorithm for adaptive control of the technical condition of power transformers using diagnostic and monitoring data. The developed algorithm implements the decision-making procedure for ensuring the reliable operation of oil-filled transformer equipment as part of the substations of electric power facilities. The decision-making procedure is based on the method of statistical Bayesian identification the states of a transformer based on the results of dissolved gas analysis (DGA) in oil. The method is characterized by high reliability of recognizing defects in the transformer and the ability to adapt the probabilities of the obtained solutions to the newly received diagnostic information. These results illustrate the effectiveness of the developed approach and the possibility of its application in the operation of oil-filled transformer equipment.
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10

Go, Jaedoo, and Minseok Song. "Adaptive disk power management for portable media players." IEEE Transactions on Consumer Electronics 54, no. 4 (November 2008): 1755–60. http://dx.doi.org/10.1109/tce.2008.4711231.

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11

Senguttuvan, R., S. Sen, and A. Chatterjee. "Multidimensional Adaptive Power Management for Low-Power Operation of Wireless Devices." IEEE Transactions on Circuits and Systems II: Express Briefs 55, no. 9 (September 2008): 867–71. http://dx.doi.org/10.1109/tcsii.2008.924370.

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12

HEO, SEWAN, YIL SUK YANG, SANG-KYUN LEE, and JONG-KEE KWON. "ADAPTIVE ENERGY MANAGEMENT SYSTEM BASED ON LOW-POWER MICROCONTROLLER WITH ENERGY HARVESTING AT MAXIMUM POWER." Journal of Circuits, Systems and Computers 21, no. 08 (December 2012): 1240020. http://dx.doi.org/10.1142/s0218126612400208.

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This paper describes an energy management system and an algorithm for an energy-aware operation. The system obtains energy from an energy harvester and manages the energy adaptively according to the monitored energy status. Based on a low-power microcontroller, the system controls the energy harvester so that it always harvests energy at maximum power and tracks it when the operating condition changes. It also controls the power consumption of all parts of the system so that they are adjusted dynamically for the management of harvested or stored energy. To manage the energy transfer to a battery, a DC–DC converter, called the energy management IC, is optimized for the operating voltage control of the harvester. In an experiment using an energy harvester and a battery modified for the system, the energy management IC fabricated in a 0.18 μm process maximizes the energy transfer power with a simple, low-power algorithm. The proposed system is verified to be more efficient for low-energy harvesting by the adaptive energy and power management.
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13

Alhasnawi, Bilal, and Basil Jasim. "Adaptive Energy Management System for Smart Hybrid Microgrids." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 73–85. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.11.

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The energy management will play an important role in the future smart grid by managing loads in an intelligent way. Energy management programs, realized via House Energy Management systems (HEMS) for smart cities, provide many benefits; consumers enjoy electricity price savings, and utility operates at reduced peak demand. This paper proposed an adaptive energy management system for islanded mode and grid-connected mode. In this paper, a hybrid system that includes distribution electric grid, photovoltaics, and batteries are employed as energy sources in the residential of the consumer in order to meet the demand. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. This paper uses home energy management system which switches between the distributed energy and the grid power sources. The home energy management system incorporates controllers for maximum power point tracking, battery charge and discharge and inverter for effective control between different sources depending upon load requirement and availability of sources at maximum powerpoint. Also, in this paper, the Maximum Power Point Tracking (MPPT) technique is applied to the photovoltaic station to extract the maximum power from hybrid power system during variation of the environmental conditions. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses loads fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses daily electrical load demand. The simulation results have verified the effectiveness and feasibility of the introduced strategy and the capability of the proposed controller for a hybrid microgrid operating in different modes.
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14

Wang, Yan, and Xiangheng Shen. "Adaptive Dynamic Power Management Policy: Two Value Alternate Basis." Research Journal of Applied Sciences, Engineering and Technology 5, no. 2 (January 11, 2013): 558–62. http://dx.doi.org/10.19026/rjaset.5.4989.

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15

Liu, Song, Yu Zhang, Seda Ogrenci Memik, and Gokhan Memik. "An Approach for Adaptive DRAM Temperature and Power Management." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 4 (April 2010): 684–88. http://dx.doi.org/10.1109/tvlsi.2009.2014842.

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16

Wang, Yan, and Xiangheng Shen. "Adaptive Dynamic Power Management Policy: Two Value Alternate Basis." Advanced Science Letters 11, no. 1 (May 30, 2012): 783–86. http://dx.doi.org/10.1166/asl.2012.3036.

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17

Khursheed, Saqib, Urban Ingelsson, Paul Rosinger, Bashir M. Al-Hashimi, and Peter Harrod. "Bridging Fault Test Method With Adaptive Power Management Awareness." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27, no. 6 (June 2008): 1117–27. http://dx.doi.org/10.1109/tcad.2008.923247.

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18

Lari, Vahid, Shravan Muddasani, Srinivas Boppu, Frank Hannig, Moritz Schmid, and Jürgen Teich. "Hierarchical power management for adaptive tightly-coupled processor arrays." ACM Transactions on Design Automation of Electronic Systems 18, no. 1 (January 2013): 1–25. http://dx.doi.org/10.1145/2390191.2390193.

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19

Salehi, Mohammad, Alireza Ejlali, and Muhammad Shafique. "Run-Time Adaptive Power-Aware Reliability Management for Manycores." IEEE Design & Test 35, no. 5 (October 2018): 36–44. http://dx.doi.org/10.1109/mdat.2017.2775738.

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20

Chen, Bo-Chiuan, Jen-Chiun Guan, and Jhih-Hong Li. "Adaptive Power Management Control of Range Extended Electric Vehicle." Energy Procedia 61 (2014): 67–70. http://dx.doi.org/10.1016/j.egypro.2014.11.908.

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21

Sakr, Walaa S., Ragab A. EL-Sehiemy, and Ahmed M. Azmy. "Adaptive differential evolution algorithm for efficient reactive power management." Applied Soft Computing 53 (April 2017): 336–51. http://dx.doi.org/10.1016/j.asoc.2017.01.004.

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22

Althunibat, Saud, Nizar Zorba, Charalabos Skianis, and Christos Verikoukis. "Power Management in Multiuser Adaptive Modulation Transmission under QoS Requirements." International Journal of Antennas and Propagation 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/730710.

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A transmitter power management mechanism is presented in this paper where Multiple-Input Multiple-Output (MIMO) Multiuser Random Beamforming with Adaptive Modulation strategy is performed by the system. The objective of the proposed mechanism is quality of service (QoS) satisfaction for the scheduled user. The QoS is represented by the application demanded data rate and symbol error rate. The power outage problem is considered and a practical method to minimize the outage probability is proposed. The obtained results are encouraging as they show a great decrease in the system power budget. The multiuser system capability is also exploited to achieve larger power saving values and smaller probability of power outage by scheduling the user with the best channel characteristics at each time instant. The amount of saved power and the power outage probability are both presented through closed-form expressions. These theoretical results are compared to computer simulations, which show very good agreement in performance.
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23

Ammari, Ahmed Chiheb. "Adaptive battery aware power management of a computer with self power-managed components." Microprocessors and Microsystems 72 (February 2020): 102947. http://dx.doi.org/10.1016/j.micpro.2019.102947.

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24

Joshi, Gauri, and Prabhat Ranjan. "CAP (Coordinated Adaptive Power) Management Technique with Adaptive Threshold Policy for Wireless Sensor Nodes." Proceedings of the Asia-Pacific Advanced Network 32 (December 13, 2011): 101. http://dx.doi.org/10.7125/apan.32.12.

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25

Choi, Hayeon, Youngkyoung Koo, and Sangsoo Park. "Adaptive operating mode management model for efficient energy harvesting systems." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014772090780. http://dx.doi.org/10.1177/1550147720907801.

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Energy harvesting technology is becoming popular concerning efficient use of Internet of Things devices, which collect energy present in nature and use it to power themselves. Although the technology is eco-friendly, it is dependent on the vagaries of the surrounding environment; the amount of energy produced is sensitive to the weather and terrain, and intermittent power threatens the system’s stability. Thus, it is essential to collect data that can determine the circumstances of the surrounding environment. Furthermore, these systems should be designed efficiently for continuous energy harvesting. This efficiency can vary depending on the system’s configuration. Core voltage levels and frequencies typically influence efficiency. To maximize system efficiency, power management with an appropriate combination of controllable factors is necessary. We design an energy harvesting system for real-time data acquisition. We propose a methodology to guide the optimal operating power stage considering various adjustable factors for efficient operation. Also, we propose an adaptive operating power mode management model, which involves selecting the optimal operating power step and the transition to a low-power mode (LPM) during idle time. The proposed model was applied to an actual energy harvesting system to demonstrate its effectiveness and facilitated the operation of the harvesting system at low power.
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26

Guan, Jen-Chiun, Bo-Chiuan Chen, and Yuh-Yih Wu. "Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles." Energies 12, no. 9 (April 28, 2019): 1610. http://dx.doi.org/10.3390/en12091610.

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The cruising distance of the range extended electric vehicle (REEV) can be further extended using a range extender, which consists of an engine and a generator, i.e., a genset. An adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) is proposed for the REEV in this paper. The desired trajectory of the state of charge (SOC) is designed based on the energy-to-distance ratio, which is defined as the difference between the initial SOC and the minimum allowable SOC divided by the remaining travel distance, for discharging the battery. A self-organizing fuzzy controller (SOFC) with SOC feedback is utilized to modify the equivalence factor, which is defined as the fuel consumption rate per unit of electric power, for tracking the desired SOC trajectory. An instantaneous cost function, that consists of the fuel consumption rate of the genset and the equivalent fuel consumption rate of the battery, is minimized to find the optimum power distribution for the genset and the battery. Dynamic programming, which is a global minimization method, is employed to obtain the performance upper bound for the target REEV. Simulation results show that the proposed algorithm is adaptive for different driving cycles and can effectively increase the fuel economy of the thermostat control strategy (TCS) by 11.1% to 16%. The proposed algorithm can also reduce average charging/discharging powers and low SOC operations for possibly extending the battery life and increasing the battery efficiency, respectively. An experiment of the prototype REEV on a chassis dynamometer is set up with the proposed algorithm implemented on a real-time controller. Experiment results show that the proposed algorithm can increase the fuel economy of the TCS by 7.8% for the tested driving cycle. In addition, the proposed algorithm can reduce the average charge/discharge powers of TCS by 7.9% and 11.7%, respectively.
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27

Mohd Zulkefli, Muhammad Fathi, Ismail Musirin, Shahrizal Jelani, Mohd Helmi Mansor, and Naeem M. S. Honnoon. "Embedded adaptive mutation evolutionary programming for distributed generation management." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 364. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp364-370.

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<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>
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28

KIM, M. G., J. CHOI, B. JUNG, and M. KANG. "Adaptive Power Management Mechanism Considering Remaining Energy in IEEE 802.16e." IEICE Transactions on Communications E90-B, no. 9 (September 1, 2007): 2621–24. http://dx.doi.org/10.1093/ietcom/e90-b.9.2621.

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29

Chung, Y. W., M. Y. Chung, and D. K. Sung. "Adaptive algorithm for mobile terminal power on/off state management." Computer Communications 24, no. 14 (September 2001): 1411–24. http://dx.doi.org/10.1016/s0140-3664(00)00334-0.

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30

WU, Qi. "Adaptive Dynamic Power Management for Non-Stationary Self-Similar Requests." Journal of Software 16, no. 8 (2005): 1499. http://dx.doi.org/10.1360/jos161499.

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31

Shih, Hung-Cheng, and Kuochen Wang. "An adaptive hybrid dynamic power management algorithm for mobile devices." Computer Networks 56, no. 2 (February 2012): 548–65. http://dx.doi.org/10.1016/j.comnet.2011.10.005.

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32

Wang, Meng, Jian Qiang Wu, and Xiao Hua Zhang. "A Self-Adaptive BMS Based on CAN-Bus for Power Li-Ion Battery." Applied Mechanics and Materials 130-134 (October 2011): 3553–56. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3553.

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A self-adaptive battery management system (BMS) based on CAN-bus for power Li-ion battery was designed in this paper. It is designed distributed and composed of sampling modules, a master module and some aid devices. The sampling module is used to sampling the voltage of the cells and the temperature in the pack. And it keeps the temperature in safe range by controlling the fans in the pack. The master module receives the information from the sampling modules, samples the current of the main road, estimates the state of charge (SOC), the state of health (SOH), controls the main road relays. The modules communicate with each other by CAN-bus. The fault and temperature management are performed adaptively. It is shown that this self-adaptive BMS extend the battery lifetime and guarantee safe operation. And the self-power consumption is very low.
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33

McDonald, Jim. "Adaptive intelligent power systems: Active distribution networks." Energy Policy 36, no. 12 (December 2008): 4346–51. http://dx.doi.org/10.1016/j.enpol.2008.09.038.

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34

Chen, Junlin, Jun-Hong Cui, and Lei Wang. "RF Power Management via Energy-Adaptive Modulation for Self-Powered Systems." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 23, no. 9 (September 2015): 1931–35. http://dx.doi.org/10.1109/tvlsi.2014.2344631.

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35

Lu, Yanchao, Donghong Wu, Bingsheng He, Xueyan Tang, Jianliang Xu, and Minyi Guo. "Rank-Aware Dynamic Migrations and Adaptive Demotions for DRAM Power Management." IEEE Transactions on Computers 65, no. 1 (January 1, 2016): 187–202. http://dx.doi.org/10.1109/tc.2015.2409847.

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36

Li, Haoran, Jiang Xu, Zhe Wang, Rafael K. V. Maeda, Peng Yang, and Zhongyuan Tian. "Workload-Aware Adaptive Power Delivery System Management for Many-Core Processors." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37, no. 10 (October 2018): 2076–86. http://dx.doi.org/10.1109/tcad.2017.2778080.

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37

Zhang, Yicheng, Jian Chen, and Yang Yu. "Distributed power management with adaptive scheduling horizons for more electric aircraft." International Journal of Electrical Power & Energy Systems 126 (March 2021): 106581. http://dx.doi.org/10.1016/j.ijepes.2020.106581.

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38

Liang, Aihua, Limin Xiao, and Li Ruan. "Adaptive workload driven dynamic power management for high performance computing clusters." Computers & Electrical Engineering 39, no. 7 (October 2013): 2357–68. http://dx.doi.org/10.1016/j.compeleceng.2013.04.026.

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39

Guan, Jen-Chiun, and Bo-Chiuan Chen. "Adaptive Power Management Strategy for a Four-Mode Hybrid Electric Vehicle." Energy Procedia 105 (May 2017): 2403–8. http://dx.doi.org/10.1016/j.egypro.2017.03.689.

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40

Liu, Hui, Xunming Li, Weida Wang, Lijin Han, Huibin Xin, and Changle Xiang. "Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 12 (December 12, 2018): 3125–46. http://dx.doi.org/10.1177/0954407018816564.

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An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.
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41

Eshuis, Jasper, and Lasse Gerrits. "The limited transformational power of adaptive governance: a study of institutionalization and materialization of adaptive governance." Public Management Review 23, no. 2 (October 17, 2019): 276–96. http://dx.doi.org/10.1080/14719037.2019.1679232.

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42

Suliman, Mohammed Yahya. "Active and reactive power flow management in parallel transmission lines using static series compensation (SSC) with energy storage." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (December 1, 2019): 4598. http://dx.doi.org/10.11591/ijece.v9i6.pp4598-4609.

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<p>The power flow controlled in the electric power network is one of the main factors that affected the modern power systems development. The Static Series Compensatior with storage energy, is a FACTS powerful device that can control the active power flow control of multiple transmission lines branches. In this paper, a simulation model of power control using static series compensator with parallel transmission lines is presented. The control system using adaptive neuro-fuzzy logic is proposed. The results show the ability of static series compensator with storage energy to control the flow of powers components "active and reactive power" in the controlled line and thus the overall power regulated between lines. </p>
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43

SAVINA, NATALIA VIKTOROVNA, IVAN ALEXANDROVICH LISOGURSKY, and LYDIA NIKOLAEVNA LISOGURSKY. "APPLICATION OF ACTIVE-ADAPTIVE NETWORK TECHNOLOGIES FOR POWER QUALITY MANAGEMENT IN ELECTRIC NETWORKS WITH TRACTION LOAD." Messenger AmSU, no. 93 (2021): 56–61. http://dx.doi.org/10.22250/jasu.93.12.

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On the basis of the system approach, the analysis of the basic technologies of the intelligent electric power system that provide adaptive management of the electric network is carried out. The expediency of their application for adaptive power quality management in electric networks with traction load by expanding the functions is shown. The variants of their configuration are proposed to improve the quality of electricity and ensure optimal parameters of electricity quality indicators.
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44

Ivanov, Konstantin Samson. "Adaptive Robotics." Applied Mechanics and Materials 656 (October 2014): 154–63. http://dx.doi.org/10.4028/www.scientific.net/amm.656.154.

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In work theoretical bases of an adaptive robotics are stated. The adaptive robotics considers creation, research and designing of robots with adaptive electric drives of modules. The adaptive drive contains the electric motor and the adaptive gear mechanism possessing property independently to change output speed of movement depending on loading. This property of adaptation is named by self-regulation. Self-regulation is carried out only for the account of mechanics and does not demand management. The adaptive drive demands smaller capacity, overcomes emergency overloads, is structurally simple, has small dimensions and weight. These advantages are especially important for intermediate modules of the manipulator. Use of adaptive drives procures high power efficiency of the robot.
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45

Joshi, Gauri, and Prabhat Ranjan. "Optimizing Power and Buffer Congestion on Wireless Sensor Nodes Using CAP (Coordinated Adaptive Power) Management Technique." International Journal of Wireless & Mobile Networks 3, no. 2 (April 30, 2011): 225–41. http://dx.doi.org/10.5121/ijwmn.2011.3218.

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46

Manjula, Venkatachalam, and Ahamed Khan Mahabub Basha. "Adaptive technique for transient stability constraints optimal power flow." International Journal of Operational Research 39, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijor.2020.108833.

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47

Hassan, Tehseen, Fei Gao, Babur Jalal, and Sheeraz Arif. "Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique." Future Internet 10, no. 3 (March 1, 2018): 25. http://dx.doi.org/10.3390/fi10030025.

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48

Dixit, Ashish, Sachin Kumar, Pooja Khanna, and Pragya. "Adaptive Power Management Prototype Employing Intelligent Scheduling of Time and Solar Tracker." Procedia Computer Science 167 (2020): 1749–60. http://dx.doi.org/10.1016/j.procs.2020.03.385.

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49

Wang, Wensi, Ningning Wang, Mike Hayes, Brendan O’Flynn, and Cian O’Mathuna. "Power management for sub-mW energy harvester with adaptive hybrid energy storage." Journal of Intelligent Material Systems and Structures 24, no. 11 (November 15, 2012): 1365–79. http://dx.doi.org/10.1177/1045389x12463464.

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50

Yao, Jianguo, Haibing Guan, Jianying Luo, Lei Rao, and Xue Liu. "Adaptive Power Management through Thermal Aware Workload Balancing in Internet Data Centers." IEEE Transactions on Parallel and Distributed Systems 26, no. 9 (September 1, 2015): 2400–2409. http://dx.doi.org/10.1109/tpds.2014.2353051.

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