• Journal of Advanced Marine Engineering and Technology
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• v.39 no.4
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• pp.381-386
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• 2015

Environmental pollution and alternative energy has attracted increasing interest. The use of diesel engines is expected to increase in the world owing to their fuel economy. The problem of air pollution emissions from marine engines is causing a major concern in many areas. An alternative fuel was introduced as an environmentally friendly fuel to reduce the toxic emissions from conventional fossil fuels. Biodiesel fuel, which is a renewable energy is highlighted as environmentally friendly energy. This energy can be operated in regular diesel engines when it is blended with invariable ratios without making changes. In this study, a bio-diesel fuel was produced from waste cooking oil and applied to a marine diesel engine to examine the effects on the characteristics of combustion. Waste cooking oil contains a high cetane number and viscosity component, a low carbon and oxygen content. As a result, the brake specific fuel consumption was increased, and the cylinder pressure, rate pressure rise and rate of heat release were decreased.

• 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 Advanced Marine Engineering and Technology
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• v.29 no.8
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• pp.883-890
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• 2005

The industrial growth and the raised living quality have led to the massive energy consumption. As a result. the polluted environment and the limited amount of energy resources emerged as serious problems to be resolved in 21st century. Especially, in the case of Korea whose imported energy dependency rate is 98.2$\%$ in 2003 and constantly increasing every rear. more than 24$\%$ of overall energy consumption is for housing and commercial use. In order to cope with the shortage of natural energy resources, it is inevitable to develop alternative sustainable energy resources including seawater heat. so that they can replace existing resources. The heat transfer air velocity 3.5 m/s is proper to consideration with the body the pipe size 200A is more suitable than look due to the air velocity quantify and the ratio of pipe surface area. And the error between experimental data with simulation is below 5.34$\%$ so the suggested equation for calculating heat transfer capacity can be used. Therefore out of many methods utilizing seawater heat. this work Presents the efficiency of using sea water heat as a resource for air-conditioners which can be converted from the outside air through the air-to-heat conversion tube . Consequently. this method provides pretty reasonable energy efficiency.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.473-481
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• 2007

Energy efficiency is one of the most critical concerns for wireless sensor networks. By allowing sensor nodes in close proximity to cooperate in transmission to form a virtual multiple-input multiple-output(MIMO) system, recent progress in wireless MIMO communications can be exploited to boost the system throughput, or equivalently reduce the energy consumption for the same throughput and BER target. However, these cooperative transmission strategies may incur additional energy cost and system overhead. In this paper, assuming that data collectors are equipped with antenna arrays and superior processing capability, energy efficiency of relevant traditional and cooperative transmission strategies: Single-input-multiple-output(SIMO), space-time block coding(STBC), and spatial multiplexing(SM) are studied. Analysis in the wideband regime reveals that, while receive diversity introduces significant improvement in both energy efficiency and spectral efficiency, further improvement due to the transmit diversity of STBC is limited, as opposed to the superiority of the SM scheme especially for non-trivial spectral efficiency. These observations are further confirmed in our analysis of more realistic systems with limited bandwidth, finite constellation sizes, and a target error rate. Based on this analysis, general guidelines are presented for optimal transmission strategy selection in system level and link level, aiming at minimum energy consumption while meeting different requirements. The proposed selection rules, especially those based on system-level metrics, are easy to implement for sensor applications. The framework provided here may also be readily extended to other scenarios or applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.62-74
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• 2020

Although the government and local governments devote efforts to activate bicycles, they only access to the supply infrastructure such as bike lanes and the public bicycle rental service centers without considering the measures to overcome the geographical constraints of slope. Therefore, this study constructs bicyclist's energy consumption estimation model through experimental methods of slope gradient and heart rate measurement and suggest the bicycle route choice model which could minimize the energy by the slope gradient. After calculating the RMSE of the estimated energy consumption by applying this model to the simulation section, it is confirmed to be 41% better than the model which does not reflect slope gradient. The results of this study are expected to be applied to the bicycle infrastructure planning that considers both longitude and transverse of bike lanes and the algorithm of bicycle route guidance system in the future.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.658-665
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• 2006

In these days, the demand of users to extend available period of mobile systems is increased according as the functions of mobile systems have been varied and the use of multimedia application has been increased. This paper proposes an integrated power management framework that considers executed workload types for effective energy management. The conventional methods use DVFS technique for CPU and DPM technique for WNIC separately or simply combine them based on the assumption that they are orthogonal one another. However, the proposed mechanism determines the kind of workload under analysis of the characteristics of workloads incoming through a WNIC. The proposed method can reduce energy consumption of system level effectively by controlling CPU and WNIC to proper power mode based on analyzed characteristics of workload. The experimental result shows the proposed method reduces energy consumption by 9% for BE (Best Effort) workload, CBR (Constant Bit Rate) workload, and Interactive workload on average and by 16% to maximum when compared with the conventional methods which simply combine DVFS technique for CPU and DPM technique for WNIC.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.688-698
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• 2016

In this paper, we investigate the achievable sum rate and energy efficiency of downlink massive multiple-input multiple-output antenna systems with zero-forcing precoding, by taking into account the randomness of user locations. Specifically, we propose two types of non-uniform user distributions, namely, center-intensive user distribution and edge-intensive user distribution. Based on these user distributions, we derive novel tight lower and upper bounds on the average sum rate. In addition, the impact of user distributions on the optimal number of users maximizing the sum rate is characterized. Moreover, by adopting a realistic power consumption model which accounts for the transmit power, circuit power and signal processing power, the energy efficiency of the system is studied. In particular, closed-form solutions for the key system parameters, such as the number of antennas and the optimal transmit signal-to-noise ratio maximizing the energy efficiency, are obtained. The findings of the paper suggest that user distribution has a significant impact on the system performance: for instance, the highest average sum rate is achieved with the center-intensive user distribution, while the lowest average sum rate is obtained with the edge-intensive user distribution. Also, more users can be served with the center-intensive user distribution.

• Journal of the Korea Society for Simulation
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• v.21 no.4
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• pp.47-56
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• 2012

In this study, we use a parallel simulator PASENS(Parallel SEnsor Network Simulator) to predict power consumption and data reception rate of the hierarchical routing protocols for sensor network - LEACH (Low-Energy Adaptive Clustering Hierarchy), TL-LEACH (Two Level Low-Energy Adaptive Clustering Hierarchy), M-LEACH (Multi hop Low-Energy Adaptive Clustering Hierarchy) and LEACH-C (LEACH-Centralized). According to simulation results, M-LEACH routing protocol shows the highest data reception rate for the wider area, since more sensor nodes are involved in the data transmission. And LEACH-C routing protocol, where the sink node considers the entire node's residual energy and location to determine the cluster head, results in the most efficient energy consumption and in the narrow area needed long life of sensor network.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.20 no.6
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• pp.67-77
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• 2017

Demands for housing has diversified recently due to low birth rate and the growth of aging population. Also, a share of idle houses and obsolete houses over 20 years old is gradually rising. Therefore, there is a need for a sustainable, environment-friendly improvement policy that is in line with a new housing paradigm and avoids full-scale new construction, such as a customized housing renovation plan considering local economic circumstances. Therefore, afforestation system applicable to buildings are assessed positively, but lack objective performance evaluation. Through one-year, long-term monitoring of replicated obsolete buildings that have poor insulation performance, this study calculated monthly average power consumption and analyzed power charges by applying pricing plans before and after the revision of progressive tax in order to examine economic effects expected by applying the afforestation system. In the obsolete buildings, the study showed that monthly average power consumption was reduced by 16.6kWh with 5.2% average reduction rate. Highest reduction was made in July at 11.3%. Aggregate monthly power consumption charges were relatively high in winter before and after the revision of progressive tax. Power charges reduction effect was highest in March when monthly power consumption was reduced to 300kWh level by applying the afforestation system.