• Journal of Power Electronics
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• v.17 no.6
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• pp.1694-1706
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• 2017

Models and experiments for magnetic resonance coupling wireless power transmission (MRC-WPT) topologies such as the chain topology and branch topology are studied in this paper. Coupling mode theory based energy resonance models are built for the two topologies. Complete energy resonance models including input items, loss coefficients, and coupling coefficients are built for the two topologies. The storage and the oscillation model of the resonant energy are built in the time domain. The effect of the excitation item, loss item, and coupling coefficients on MRC systems are provided in detail. By solving the energy oscillation time domain model, distance enhancing models are established for the chain topology, and energy relocating models are established for the branch topology. Under the assumption that there are no couplings between every other coil or between loads, the maximum transmission capacity conditions are found for the chain topology, and energy distribution models are established for the branch topology. A MRC-WPT experiment was carried out for the verification of the above model. The maximum transmission distance enhancement condition for the chain topology, and the energy allocation model for the branch topology were verified by experiments.

• Environmental and Resource Economics Review
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• v.31 no.3
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• pp.367-391
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• 2022

We develop an energy-economy Bayesian DSGE model with the two sectors of electricity generations-traditional (fossil, nuclear) and renewable energy. Under imperfect substitutability between the two sectors, a technological shock on renewable energy sectors does not sufficient to facilitate energy conversion and reduce greenhouse gas emissions. Technology innovation on greenhouse gas emission reduction is also required. More importantly, sufficient investment should be derived by a well-functioning electricity market where electricity price plays a signal role in efficient allocation of resources. Indeed, market rigidities cause reduced consumption.

• Journal of Communications and Networks
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• v.18 no.6
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• pp.873-883
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• 2016

In this paper, we focus on maximizing weighted sum energy efficiency (EE) for a multi-cell multi-user channel. In order to solve this non-convex problem, we first decompose the original problem into a sequence of parallel subproblems which can optimized separately. For each subproblem, a base station employs dirty paper coding to maximize the EE for users within a cell while regulating interference induced to other cells. Since each subproblem can be transformed to a convex multiple-access channel problem, the proposed method provides a closed-form solution for power allocation. Then, based on the derived optimal covariance matrix for each subproblem, a local optimal solution is obtained to maximize the sum EE. Finally, simulation results show that our algorithm based on non-linear precoding achieves about 20 percent performance gains over the conventional linear precoding method.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.109-116
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• 2018

SSDs that are not allowed in-place update within the allocated page cause another allocation of a new page that will replace the previous page at the moment data modification occurs. This intrinsic characteristic of SSDs requires many changes to the existing HDD-based IO theory. In this paper, we conduct a performance comparison of FTL caching strategy in perspective of cache hashing (Global vs. grouped) and caching algorithm (LRU vs. NUR) through a simulation. Experimental results show that in terms of energy consumption for flash operation the grouped management of cache is not suitable and NUR algorithm is superior to LRU algorithm. In particular, we found that the cache hit ratio of LRU algorithm is about 10% point higher than that of NUR algorithm while the energy consumption of LRU algorithm is about 32% high.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.673-694
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• 2021

In this paper, we intend to study the energy efficiency (EE) optimization for a simultaneous wireless information and power transfer (SWIPT)-based distributed antenna system (DAS). Firstly, a DAS-SWIPT model is formulated, whose goal is to maximize the EE of the system. Next, we propose an optimal resource allocation method by means of the Karush-Kuhn-Tucker condition as well as an ergodic method. Considering the complexity of the ergodic method, a suboptimal scheme with lower complexity is proposed by using an antenna selection scheme. Numerical results illustrate that our suboptimal method is able to achieve satisfactory performance of EE similar to an optimal one while reducing the calculation complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.489-498
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• 2012

Recently, as the interest in Green IT is exponentially increased, EPON with sleep mode has been studied to reduce energy consumption in access network. In oder to guarantee cyclic sleep for ONU(Optical Network Units), EPON with sleep mode transmits upstream and downstream data at the same time. However, since conventional algorithms for sleep mode in EPON allocate bandwidth to each ONU according to upstream bandwidth request, the QoS of downstream data is not guaranteed when the offered load of OLT is larger than that of ONU. In this paper, we propose a bandwidth allocation algorithm for improving QoS in EPON with sleep mode. The proposed algorithm compares its size with an upstream request of ONU when a downstream buffer in the OLT exceeds a QoS threshold. And then it allocates selectively a bandwidth that satisfies the required QoS between the bandwidth request of ONU and OLT. Therefore, the proposed algorithm can save energy through cyclic sleep of ONUs while guaranteeing the QoS of up/downstream data. In order to evaluate the proposed algorithm, we perform simulation in terms of total sleep time of ONUs, queueing delay between OLT and ONU, and the utilization of allocated bandwidth at OLT through OPNET.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.146-151
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• 2017

This paper proposes a transmission algorithm that optimizes transmission power and sub-channel allocation to maximize energy efficiency considering characteristics of the channel impedance of power lines in power line communication systems. Since the received power at the receiver is influenced by the characteristics of the power line channel, it is necessary to consider channel characteristics when developing a transmission strategy in a power line communication systems. In addition, the energy efficiency should be optimized while meeting the practical constraints, such as the maximum transmission power limit of the transmitter and minimum quality of service for each user. In the computer simulation, we confirm that the energy efficiency of the proposed algorithm is improved compared to baseline schemes.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.160-169
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• 2021

Background: Dual-energy X-ray images (DEI) can distinguish or improve materials of interest in a two-dimensional radiographic image, by combining two images obtained from separate low and high energies. The concepts of DEI performance describing the performance of double-exposure DEI systems in the Fourier domain been previously introduced, however, the performance of double-exposure DEI itself in terms of various parameters, has not been reported. Materials and Methods: To investigate the DEI performance, signal-difference-to-noise ratio, modulation transfer function, noise power spectrum, and noise equivalent quanta were used. Low- and high-energy were 60 and 130 kVp with 0.01-0.09 mGy, respectively. The energy-separation filter material and its thicknesses were tin (Sn) and 0.0-1.0 mm, respectively. Noise-reduction (NR) filtering used the Gaussian-filter NR, median-filter NR, and anti-correlated NR. Results and Discussion: DEI performance was affected by Sn-filter thickness, weighting factor, and dose allocation. All NR filtering successfully reduced noise, when compared with the dual-energy (DE) images without any NR filtering. Conclusion: The results indicated the significance of investigating, and evaluating suitable DEI performance, for DE images in chest radiography applications. Additionally, all the NR filtering methods were effective at reducing noise in the resultant DE images.

• New & Renewable Energy
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• v.19 no.2
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• pp.13-22
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• 2023

With the increased interest in renewable energy, various hydrogen production technologies have been developed. Hydrogen production can be classified into green, blue, gray, and pink hydrogen depending on the production method; each method has different technical performance, costs, and CO₂ emission characteristics. Hence, selecting the technology priorities that meet the company strategy is essential to develop technologically and economically feasible projects and achieve the national carbon neutrality targets. In addition, in early development technologies, analyzing the technology investment priorities based on the company's strategy and establishing investment decisions such as budget and human resources allocation is important. This study proposes a method of selecting priorities for various hydrogen production technologies as a specific implementation plan to achieve the national carbon neutrality goal. In particular, we analyze key performance indicators for technology, economic feasibility, and environmental performance by various candidate technologies and suggest ways to score them. As a result of the analysis using the aforementioned method, the priority of steam methane reforming (SMR) technology combined with carbon capture & storage (CCS) was established to be high in terms of achieving the national carbon neutrality goal.