• Proceedings of the KSR Conference
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• 2005.11a
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• pp.107-112
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• 2005

When it comes to automatic operation control of urban rail vehicle, a PID control makes it run between stations within the fixed time and stop exactly at the stop sign on the platform, satisfying jerk limit. An optimal control is applied to automatic operation performance control to minimize energy consumption while the urban rail vehicle satisfies automatic operation condition on this paper. The control performance in terms of energy minimization along with the constraint on precision stops is compared between the optimal control and PID control.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.65-72
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• 2010

In ad-hoc network, the technique to efficiently consume the limited amounts of energy is an important issue since the wireless terminal node is operated on batteries as their energy resource. In order to extend the system lifetime, through a balanced energy consumption, we must delay the situation in which a particular terminal node's energy is depleted and results in system disconnection. Also, the link, which has low reliability due to the mobility of the node, should be avoided considering the key element when setting up the route. The proposed CMLR method in this paper enables to increase the efficiency of energy consumption with a new cost function considering the residue energy of node, error rate of link, and transmission energy consumption. This method is extending the network lifetime and increasing the energy efficiency by compromising the value between the minimization of the transmission energy consumption and maximization of the node's lifetime. Through the simulations the proposed CMLR algorithm was verified by showing better performance over the conventional methods in terms of network lifetime and path efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

• Journal of Computing Science and Engineering
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• v.4 no.3
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• pp.189-206
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• 2010

It is generally accepted that dynamic voltage scaling (DVS) is one of the most effective techniques of energy minimization for real-time applications in embedded system design. The effectiveness comes from the fact that the amount of energy consumption is quadractically proportional to the voltage applied to the processor. The penalty is the execution delay, which is linearly and inversely proportional to the voltage. According to the granularity of tasks to which voltage scaling is applied, the DVS problem is divided into two subproblems: inter-task DVS problem, in which the determination of the voltage is carried out on a task-by-task basis and the voltage assigned to the task is unchanged during the whole execution of the task, and intra-task DVS problem, in which the operating voltage of a task is dynamically adjusted according to the execution behavior to reflect the changes of the required number of cycles to finish the task before the deadline. Frequent voltage transitions may cause an adverse effect on energy minimization due to the increase of the overhead of transition time and energy. In addition, DVS needs to be carefully applied so that the dynamically varying chip temperature should not exceed a certain threshold because a drastic increase of chip temperature is highly likely to cause system function failure. This paper reviews representative works on the theoretical solutions to DVS problems regarding inter-task DVS, intra-task DVS, voltage transition, and thermal-aware DVS.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.36-44
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• 2014

Exploiting the energy-delay tradeoff for energy saving is critical for developing green wireless communication systems. In this paper, we investigate the delay-constrained energy-efficient packet transmission. We aim to minimize the energy consumption of multiple randomly arrived packets in an additive white Gaussian noise channel subject to individual packet delay constraints, by taking into account the practical on-off circuit power consumption at the transmitter. First, we consider the offline case, by assuming that the full packet arrival information is known a priori at the transmitter, and formulate the energy minimization problem as a non-convex optimization problem. By exploiting the specific problem structure, we propose an efficient scheduling algorithm to obtain the globally optimal solution. It is shown that the optimal solution consists of two types of scheduling intervals, namely "selected-off" and "always-on" intervals, which correspond to bits-per-joule energy efficiency maximization and "lazy scheduling" rate allocation, respectively. Next, we consider the practical online case where only causal packet arrival information is available. Inspired by the optimal offline solution, we propose a new online scheme. It is shown by simulations that the proposed online scheme has a comparable performance with the optimal offline one and outperforms the design without considering on-off circuit power as well as the other heuristically designed online schemes.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.543-556
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• 2018

Zero energy building was attended for energy consumption minimization by the energy saving technology about building heating and cooling energy consumption and the renewable energy production facility. So the government has supported the zero energy building supply for decreasing green gas emissions. The study about inventory of zero energy building has many proceeding. That inventory need the information of material and equipment. So information of material and equipment about zero energy building must be included for the zero energy building realization. Actually the database of zero energy building inventory construction through the inventory established studies has difficult because the database need many information. In this study, author proposed the test methods and performance reference for upload at inventory. It was constructed to material - module - package. Also the author analyzed the construction of the technical package for zero energy building. The author separated performance category to the energy performance for energy analysis and other performance for confirmed the durability, stability and etc. This performance category proposed the table. The test methods of material and equipment in the passive package and active package proposed to the international standard and korea standard basically korea standard. Also the performance reference was proposed to korea legal standard and various standard by this study results. And the authors proposed the table of performance value, test methods, performance reference. By result of this study, the test methods and performance reference will be used the basic data for inventory of zero energy building.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.280-285
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• 2022

In order to ensure maximum capacity at a given frequency resource, the number of antennas must be increased. The increase in antennas means that such guaranteed channel resources can be used as an increase in channel capacity by aquiring another channel resource. In order to aggregate antennas in such a situation where there are a plurality of antennas, a problem of miniaturizing and integrating antennas must be accompanied. In this situation, in order to efficiently allocate channel resources and antenna resources in limited device resources, the problem of antenna selection and user scheduling was considered and solved together. By numerical simulation results, the proposed algorithm was proven to effectively reduce 34 % power consumption in averagewith increase in antennas.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.13-18
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• 2019

The high energy penalty in amine-based post-combustion CO₂ capture process is hampering its industrial scale application. An advanced process is designed by intensive heat integration within the conventional process to reduce the stripper duty. The study presents the technical feasibility for stripper duty reduction by intensive heat integration in CO₂ capture process. A rigorous rate-based model has been used in Aspen Plus® to simulate conventional and advanced process for a 300 MW coal-based power plant. Several design and operational parameters like split ratio, stripper inter-heater location and flowrate were studied to find the optimum values. The results show that advanced configuration with heat integration can reduces the stripper heat by 14%.

• Advances in Automotive Engineering
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• v.1 no.1
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• pp.105-121
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• 2018

The hybrid electric powertrain is a robust solution that allows for major improvements in both fuel economy and emission reduction. In the present study, a through-the-road hybrid vehicle model with an electric motor driving the rear axle and an Internal Combustion Engine (ICE) driving the front axle has been constructed. We then present a systematic method for the determination of a real time applicable optimal Energy Management Strategy (EMS) for a hybrid road vehicle. More precisely, we compare the performance of rule-based EMS strategies to an optimization-based strategy, namely ECMS (Equivalent Consumption Minimization Strategy). The comparison is conducted in parallel with a parameterization of the size of the internal combustion engine and the implementation of a Continuously Variable Transmission (CVT) that allows following the line of best fuel economy. For the FTP-75 driving cycle, the constrained engine On-off control algorithm is shown to offer a 28% improvement potential of fuel consumption compared to the conventional internal combustion engine while the ECMS strategy achieves an improved potential of nearly 33%.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.