• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.34-49
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• 2015

Market means of spectrum trading have been utilized as a vital method of spectrum sharing and access in future cognitive radio system. In this paper, we consider the spectrum trading with multiple primary carrier providers (PCP) leasing the spectrum to multiple secondary carrier providers (SCP) for a short period of time. Several factors including the price of the resource, duration of leasing, and the spectrum quality guides the proposed model. We formulate three trading policies based on the game theory for dynamic spectrum access in a LTE based cognitive radio system (CRS). In the first, we consider utility function based resource sharing (UFRS) without any knowledge of past transaction. In the second policy, each SCP deals with PCP using a non-cooperative resource sharing (NCRS) method which employs optimal strategy based on reinforcement learning. In variation of second policy, third policy adopts a Nash bargaining while incorporating a recommendation entity in resource sharing (RERS). The simulation results suggest overall increase in throughput while maintaining higher spectrum efficiency and fairness.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Gastric Cancer
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• v.15 no.1
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• pp.29-38
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• 2015

Purpose: Before expanding our indications for laparoscopic gastrectomy to advanced gastric cancer and adopting reduced port laparoscopic gastrectomy, we analyzed and audited the outcomes of laparoscopy-assisted distal gastrectomy (LADG) for adenocarcinoma; this was done during the adoptive period at our institution through the comparative analysis of short-term surgical outcomes and learning curves (LCs) of two surgeons with different careers. Materials and Methods: A detailed comparative analysis of the LCs and surgical outcomes was done for the respective first 95 and 111 LADGs performed by two surgeons between July, 2006 and June, 2011. The LCs were fitted by using the non-linear ordinary least squares estimation method. Results: The postoperative morbidity and mortality rates were 14.6% and 0.0%, respectively, and there was no significant difference in the morbidity rates (12.6% vs. 16.2%, P=0.467). More than 25 lymph nodes were retrieved by each surgeon during LADG procedures. The LCs of both surgeons were distinct. In this study, a stable plateau of the LC was not achieved by both surgeons even after performing 90 LADGs. Conclusions: Regardless of the experience with gastrectomy or laparoscopic surgery for other organs, or the age of surgeon, the outcome was quite acceptable; the learning process differ according to the surgeon's experience and individual characteristics.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.552-560
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• 2021

With the recent development of the art distribution system, interest in art investment is increasing rather than seeing art as an object of aesthetic utility. Unlike stocks and bonds, the price of artworks has a heterogeneous characteristic that is determined by reflecting both objective and subjective factors, so the uncertainty in price prediction is high. In this study, we used LSTM Recurrent Neural Network deep learning model to predict the auction winning price by inputting the artist, physical and sales charateristics of the Korean artist. According to the result, the RMSE value, which explains the difference between the predicted and actual price by model, was 0.064. Painter Lee Dae Won had the highest predictive power, and Lee Joong Seop had the lowest. The results suggest the art market becomes more active as investment goods and demand for auction winning price increases.

• Journal of Ginseng Research
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• v.48 no.1
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• pp.59-67
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• 2024

Background: Alzheimer's disease (AD) has memory impairment associated with aggregation of amyloid plaques and neurofibrillary tangles in the brain. Although anti-amyloid β (Aβ) protein antibody and chemical drugs can be prescribed in the clinic, they show adverse effects or low effectiveness. Therefore, the development of a new drug is necessarily needed. We focused on the cognitive function of Panax ginseng and tried to find active ingredient(s). We isolated panaxcerol D, a kind of glycosyl glyceride, from the non-saponin fraction of P. ginseng extract. Methods: We explored effects of acute or sub-chronic administration of panaxcerol D on cognitive function in scopolamine- or Aβ_25-35 peptide-treated mice measured by several behavioral tests. After behavioral tests, we tried to unveil the underlying mechanism of panaxcerol D on its cognitive function by Western blotting. Results: We found that pananxcerol D reversed short-term, long-term and object recognition memory impairments. The decreased extracellular signal-regulated kinases (ERK) or Ca²⁺/calmodulin-dependent protein kinase II (CaMKII) in scopolamine-treated mice was normalized by acute administration of panaxcerol D. Glial fibrillary acidic protein (GFAP), caspase 3, NF-kB p65, synaptophysin and brainderived neurotrophic factor (BDNF) expression levels in Aβ_25-35 peptide-treated mice were modulated by sub-chronic administration of panaxcerol D. Conclusion: Pananxcerol D could improve memory impairments caused by cholinergic blockade or Aβ accumulation through increased phosphorylation level of ERK or its anti-inflammatory effect. Thus, panaxcerol D as one of non-saponin compounds could be used as an active ingredient of P. ginseng for improving cognitive function.

• Korean Medical Education Review
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• v.17 no.3
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• pp.105-109
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• 2015

The purpose of this study is to examine the concept and importance of the hidden curriculum, which has an influence on the learning, culture, and identity formation of medical students, and to examine the student culture related to the hidden curriculum. The hidden curriculum can be defined from various perspectives. However, these definitions commonly include the concept of the whole experience students gain from school life in implicit ways, even though the school does not intend it. The hidden curriculum is related to non-cognitive areas and the culture formation of students in various way, including positive and negative content, and is important since once this curriculum is formed, it has a long-term impact. Therefore, it is necessary to consider not only the formal curriculum but also the hidden curriculum in order to apprehend the overall educational outcome of medical school. For this purpose, schools need to not only support studies on the hidden curriculum but also to endeavor to provide faculty and staff with educational and administrative support so that they can understand the hidden curriculum and be equipped as a role model. Furthermore, medical students need to endeavor to form a positive student culture in order to establish an appropriate identity as a doctor in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• Journal of the Korea Safety Management & Science
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• v.10 no.1
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• pp.107-116
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• 2008

Nowadays, BSC presented by alpan is observed by many enterprises and is being operated gradually. BSC includes non-financial factor as well as financial factor in performance assessment and it is a tool that will be able to evaluate even strategy of long-term view. In point of performance management, BSC brings in relief importance on non-financial performance as well as financial performance and it shares with viewpoint of 4 things of financial viewpoint, customer viewpoint, internal process view point, learning and growth viewpoint. then these make an array with vision and strategy of organization by causal relationship, it presented necessity of performance control on organization as center on KPI of inner of each viewpoint. Thus, study on measures and control of management performance is progressed actively and is accomplishing much development. This study is aimed at calculation of weight that is able to reflect its importance about AHP on KPI of each viewpoint. The purpose of this study is to present desirable performance measurement model and to give a weight in consideration of working-level character.