• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1982-1985
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• 2003

Future cellular networks will mainly be driven by, high quality channels, high band with utilization, low power consumption and efficient network management. For a given channel allocation, the capacity and quality of communication of cellular radio systems using CDMA(Code Division Multiple Access) can be increased by using a transmitter power control scheme to combat the near-far problem. Centralized power control schemes or distributed ones to maximize the minimum signal-to-interference in each user of CDMA wireless network have been investigated. This paper has proposed a distributed power control algorithm, which employs an adaptive search scheme, in order to solve quickly the linear systems of equations for power update in CDMA cellular radio systems. The simulation results show that the proposed scheme has faster convergence rate than the typical bang-bang type of distributed power control algorithm, which has been much used as a reference algorithm in IS-95A and W-CDMA communication network.

• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.3
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• pp.174-179
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• 2007

In this paper, we present a optimal module selection using genetic algorithm under the power, area, delay constraint. The proposed algorithm use the way of optimal module selection it will be able to minimize power consumption. In the comparison and experimental results, The proposed application algorithm reduce maximum power saving up to 26.9% comparing to previous non application algorithm, and reduce minimum power saving up to 9.0%. It also show the average power saving up to 15.525% and proved the power saving efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2281-2301
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• 2014

In wireless ad hoc networks the nodes focus on achieving the maximum SINR for efficient data transmission. In order to achieve maximum SINR the nodes culminate in exhausting the battery power for successful transmissions. This in turn affects the successful transmission of the other nodes as the maximum transmission power opted by each node serves as a source of interference for the other nodes in the network. This paper models the choice of power for each node as a non cooperative game where the throughput of the network with respect to the consumption of power is formulated as a utility function. We propose an adaptive pricing scheme that encourages the nodes to use minimum transmission power to achieve target SINR at the Nash equilibrium and improve their net utility in multiuser scenario.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.13-24
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• 2019

The key concept of NOMA among 5G network technologies is to set the power allocation coefficient for each node. In this study, we implemented the algorithm that calculates the uplink/downlink power allocation coefficients which is the key concept of NOMA technology through analysis of minimum SNR required for successful decoding at the receiver, based on Full Duplex NOMA relay system. The performance comparison between the proposed algorithm and the existing power allocation methods is performed and the performance is confirmed in terms of ergodic sum capacity and outage probability.

• Journal of IKEEE
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• v.13 no.1
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• pp.71-76
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• 2009

As one of the most important requirements for wireless sensor networks, prolonging network lifetime can be realized by minimizing energy consumption in cluster heads as well as sensor nodes. While most of the previous researches have focused on the energy of sensor nodes, we devote our attention to cluster heads because they are most dominant source of power consumption in the cluster-based sensor networks. Therefore, we seek to minimize energy consumption by minimizing the maximum(MINMAX) energy dissipation at each cluster heads. This work requires energy-efficient clustering of the sensor nodes while satisfying given energy constraints. In this paper, we present a constraint satisfaction modeling of cluster-based routing in a heterogeneous sensor networks because mixed integer programming cannot provide solutions to this MINMAX problem. Computational experiments show that substantial energy savings can be obtained with the MINMAX algorithm in comparison with a minimum total energy(MTE) strategy.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.75-80
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• 2020

There are so many low power MAC protocols for wireless sensor network. IEEE802.15.4 among them has disadvantage of a large power consumption for synchronization. To save power consumption it use the superframe operation alternating sleep mode and awake mode. But latency is longer result from superframe operation. B-MAC can have shorter latency according to check interval. But transmitter consumes more power because of long preamble. And receiver is suffering from overhearing. In this paper, we use the adaptive check interval scheme[1] of B-MAC for enhancing the power consumption. Its maximum throughput and minimum delay is evaluated by comparing the proposed scheme with a typical single channel IEEE802.15.4.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.2
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• pp.7-13
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• 2013

The portion of lighting power consumption for our country is up to 20~30% of the total amount. Currently, most of the DSM(Demand side management) of lighting power is delivered in supply of high efficient lighting to customers. On the contrary, applications of lighting power to power load leveling are still rare. In this paper, the simulations for various control types of lighting power for load leveling are tried, and we obtained the cost effective and optimal control method through that. This simulation was executed in test office with fluorescent light by us based on applying 8 control types(on, off & dimming), considering customers' satisfaction, for instance, minimum intensity of illumination allowed. According to the result of this test, we found that mixed type(chessboard(on-off) plus dimming control(10%)) is most effective.

• Food Science and Preservation
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• v.6 no.1
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• pp.71-75
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• 1999

Power consumption, mesh size, moisture content, color difference, amylogram of rice flour milled with water soaked rice were compared with that of rice using dry pin mil process. Maximum water absorbance of rice was 35% for 2.5 hr. Power consumption to mill the soaked rice was less than of dry rice by 6.9kW/100Kg. Moisture content of rice flour from the water soaked rice was 2% higher than that of rice flour from dry rice. Population of flour particle was 52.9% of 60 mesh and 32.6% of 60∼80mesh. Gelatinization temperature of rice flour from the water soaked rice was 30C lower than that of rice flour from dry rice. Maximum and minimum viscosity of rice flour from the water soaked rice after boiling were 296 cps and 158 cps, independently. Brightness and whiteness of the rice flour from the water soaked rice were increased upto 10hr soaking and decreased after 17hr soaking. Brightness and whiteness of the rice flour were 96.17 and 96.02, independently.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.