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Jung, Jaehoon,Lee, Sang-Rim,Lee, Inkyu INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2017 IEEE Transactions on Wireless Communications Vol. No.
<P>In this paper, we investigate an energy efficiency (EE) maximization problem in multiple input single output broadcast channels. The optimization problem in this system model is difficult to solve in general, since it is in non-convex fractional form. Hence, conventional algorithms have addressed the problem in an iterative manner for each channel realization, which leads to high computational complexity. To tackle this complexity issue, we propose a new simple method by utilizing the fact that EE maximization becomes identical to spectral efficiency (SE) maximization for the region of the power below a certain transmit power termed as saturation power. In order to calculate the saturation power, we first introduce upper and lower bounds of the EE performance by adopting a maximal ratio transmission beamforming strategy. Then, we propose an efficient way to compute the saturation power for the EE maximization problem. Once we determine the saturation power in advance, we can transform the EE maximization problem into a simplified sub-optimal EE problem, which can be solved by the SE maximization schemes with low complexity. The derived saturation power is parameterized by employing random matrix theory, which relies only on the second-order channel statistics. Hence, this approach needs much lower computational complexity compared with a conventional scheme, which requires instantaneous channel state information. Numerical results validate that the proposed algorithm achieves near optimal EE performance with significantly reduced complexity.</P>
Jaehoon Jung,Byoung-Duk Lim,Hyoji Shin,Jae Hee Lee 한국청각언어재활학회 2023 Audiology and Speech Research Vol.19 No.2
Purpose: This study examined the spatial separation benefit (SSB) and fluctuating masker benefit (FMB) for sentence-in-noise recognition in normal-hearing (NH) and hearing-impaired (HI) listeners. Methods: Twenty NH listeners and 10 HI listeners who were regular hearing-aid wearers participated in this study. To measure the SSB and the FMB, the Korean Matrix sentence-in-noise scores were obtained using different types of noise (steady-state speech-shaped noise, three sinusoidally amplitude-modulated noises) when the noise was colocated with the target source or was spatially separated by 30° or 60°. Results: For the NH group, the spatial separation between the target and masker was beneficial, regardless of the type of noise. Among the four types of noise, NH listeners performed poorer with speech-shaped noise than with other modulated noises, yielding a substantial improvement in speech-in-noise resulting from the masker’s fluctuation. The amount of SSB or FMB depended on the signal-to-noise ratios for the NH listeners. For HI listeners, the spatial separation was also advantageous in general, and their SSB was slightly greater in unfavorable listening conditions. However, the HI listeners hardly took advantage of the regular temporal dips of the modulated noise, even with the use of their hearing aids. Conclusion: NH listeners benefited from the fluctuation of the masker as well as the spatial separation between sound sources. A positive spatial separation benefit for HI listeners was only observed in an adverse listening condition. Regardless of the modulation rate, HI listeners received little benefit from glimpses of the target speech in the dips of the fluctuating masker. This result can be considered when planning audiological evaluation and rehabilitation for HI listeners.
Efficient Global Optimization of Periodic Plasmonic Nanoslit Array Based on Quality Factor Analysis
Jaehoon Jung Optical Society of Korea 2023 Current Optics and Photonics Vol.7 No.3
An efficient global optimization approach for a periodic plasmonic nanoslit array based on extraordinary optical transmission within an acceptable time range is proposed using 𝚀 factor analysis method. The particle swarm optimization is employed as a global optimization tool. The figure of merit is defined as a product of transmission peak value and 𝚀 factor. The design variables are the slit width, height, and period of the slit array, respectively. The optical properties such as transmission spectrum and bandwidth are calculated rigorously using the finite element method.
Jung, Yoonhwa,Jung, Jaehoon,Kim, Byungil,Han, SangUk Elsevier 2020 JOURNAL OF CLEANER PRODUCTION Vol.250 No.-
<P><B>Abstract</B></P> <P>The sites selected for solar PV facilities significantly affect the amount of electric power that can be generated over the long term. Therefore, predicting the power output of a specific PV plant is important when evaluating potential PV sites. However, whether prediction models built with data from existing PV plants can be applied to other plants for long-term power forecasting remains poorly understood. In this case, topographical and meteorological conditions, which differ among sites and change over time, make it challenging to accurately estimate the potential for energy generation at a new site. This study proposes a monthly PV power forecasting model to predict the amount of PV solar power that could be generated at a new site. The forecasting model is trained with time series datasets collected over 63 months from 164 PV sites with data such as the power plant capacity and electricity trading data, weather conditions, and estimated solar irradiation. Specifically, a recurrent neural network (RNN) model with long short-term memory was built to recognize the temporal patterns in the time series data and tested to evaluate the forecasting performance for PV facilities not used in the training process. The results show that the proposed model achieves the normalized root-mean-square-error of 7.416% and the mean absolute-percentage-error (MAPE) of 10.805% for the testing data (i.e., new plants). Furthermore, when the previous 10 months’ data were used, the temporal patterns were well captured for forecasting, with a MAPE of 11.535%. Thus, the proposed RNN approach successfully captures the temporal patterns in monthly data and can estimate the potential for power generation at any new site for which weather information and terrain data are available. Consequently, this work will allow planning officials to search for and evaluate suitable locations for PV plants in a wide area.</P> <P><B>Highlights</B></P> <P> <UL> <LI> An LSTM-RNN-based forecasting model is presented for investigation of PV sites. </LI> <LI> Time series data of spatial and meteorological conditions depict input variables. </LI> <LI> Monthly solar photovoltaic power generation at any specific site can be predicted. </LI> <LI> nRMSE of 7.416% is achieved for long-term power prediction of new candidate sites. </LI> </UL> </P>