• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.12C
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• pp.1660-1668
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• 2004

This paper proposes a Voice Activity Detection (VAD) algorithm based on Radial Basis Function (RBF) network using dual threshold. The k-means clustering and Least Mean Square (LMS) algorithm are used to upade the RBF network to the underlying speech condition. The inputs for RBF are the three parameters in a Code Exited Linear Prediction (CELP) coder, which works stably under various background noise levels. Dual hangover threshold applies in BRF-VAD for reducing error, because threshold value has trade off effect in VAD decision. The experimental result show that the proposed VAD algorithm achieves better performance than G.729 Annex B at any noise level.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.390-395
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• 2005

Sunspots are dark areas that grow and decay on the lowest level of the sun that is visible from the Earth. Shot-term predictions of solar activity are essential to help plan missions and to design satellites that will survive for their useful lifetimes. This paper presents a parallel-structure fuzzy system(PSFS) for prediction of sunspot number time series. The PSFS consists of a multiple number of component fuzzy systems connected in parallel. Each component fuzzy system in the PSFS predicts future data independently based on its past time series data with different embedding dimension and time delay. An embedding dimension determines the number of inputs of each component fuzzy system and a time delay decides the interval of inputs of the time series. According to the embedding dimension and the time delay, the component fuzzy system takes various input-output pairs. The PSFS determines the final predicted value as an average of all the outputs of the component fuzzy systems in order to reduce error accumulation effect.

• Genomics & Informatics
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• v.14 no.2
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• pp.46-52
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• 2016

Diverse somatic mutations have been reported to serve as cancer drivers. Recently, it has also been reported that epigenetic regulation is closely related to cancer development. However, the effect of epigenetic changes on cancer is still elusive. In this study, we analyzed DNA methylation data on colon cancer taken from The Caner Genome Atlas. We found that several promoters were significantly hypermethylated in colon cancer patients. Through clustering analysis of differentially methylated DNA regions, we were able to define subgroups of patients and observed clinical features associated with each subgroup. In addition, we analyzed the functional ontology of aberrantly methylated genes and identified the G-protein-coupled receptor signaling pathway as one of the major pathways affected epigenetically. In conclusion, our analysis shows the possibility of characterizing the clinical features of colon cancer subgroups based on DNA methylation patterns and provides lists of important genes and pathways possibly involved in colon cancer development.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• Corrosion Science and Technology
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• v.20 no.2
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• pp.94-104
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• 2021

Atmospheric corrosion severity associated with aircraft parking environment was studied using metallic specimens, and temperature and humidity sensors installed at each aircraft operating base. Data were analyzed after a year of exposure. Silver was used to measure chloride deposition by integrating X-ray photoelectron spectroscopy depth profiles. Carbon steel was utilized to determine the corrosion rate by measuring the weight loss. The time of wetness was determined using temperature and humidity sensor data. Analysis of variance followed by Tukey's "honestly significant difference" test indicated that atmospheric environment inside the shelter varied significantly from that of unsheltered parking environment. The corrosion rate of unsheltered area also varies with the roof. Hierarchical clustering analysis of the measured data was used to classify air bases into groups with similar atmospheric corrosion. Bases where aircraft park at a shelter can be grouped together regardless of geographical location. Unsheltered bases located inland can also be grouped together with sheltered bases as long as the aircraft are parked under the roof. Environmental severity index was estimated using collected data and validated using the measured corrosion rate.

• The Plant Pathology Journal
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• v.35 no.3
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• pp.200-207
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• 2019

Fusarium head blight (FHB) is a devastating wheat disease with a significant economic impact. Fhb1 is the most important large effect and stable QTL for FHB resistance. A pore-forming toxin-like (PFT) gene was recently identified as an underlying gene for Fhb1 resistance. In this study, we developed and validated a PFT-based Kompetitive allele specific PCR (KASP) marker for Fhb1. The KASP marker, PFT_KASP, was used to screen 298 diverse wheat breeding lines and cultivars. The KASP clustering results were compared with gelbased gene specific markers and the widely used linked STS marker, UMN10. Eight disagreements were found between PFT_KASP and UMN10 assays among the tested lines. Based on the genotyping and sequencing of genes in the Fhb1 region, these genotypes were found to be common with a previously characterized susceptible haplotype. Therefore, our results indicate that PFT_KASP is a perfect diagnostic marker for Fhb1 and would be a valuable tool for introgression and pyramiding of FHB resistance in wheat cultivars.

• Journal of Ginseng Research
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• v.45 no.3
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• pp.380-389
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• 2021

Metabolic syndrome (MS) refers to a clustering of at least three of the following medical conditions: high blood pressure, abdominal obesity, hyperglycemia, low high-density lipoprotein level, and high serum triglycerides. MS is related to a wide range of diseases which includes obesity, diabetes, insulin resistance, cardiovascular disease, dyslipidemia, or non-alcoholic fatty liver disease. There remains an ongoing need for improved treatment strategies for MS. The most important risk factors are dietary pattern, genetics, old age, lack of exercise, disrupted biology, medication usage, and excessive alcohol consumption, but pathophysiology of MS has not been completely identified. Korean Red Ginseng (KRG) refers to steamed/dried ginseng, traditionally associated with beneficial effects such as anti-inflammation, anti-fatigue, anti-obesity, anti-oxidant, and anti-cancer effects. KRG has been often used in traditional medicine to treat multiple metabolic conditions. This paper summarizes the effects of KRG in MS and related diseases such as obesity, cardiovascular disease, insulin resistance, diabetes, dyslipidemia, or non-alcoholic fatty liver disease based on experimental research and clinical studies.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.

• Human Ecology Research
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• v.62 no.1
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• pp.69-80
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• 2024

This study explored the relationship between maternal early parental attachment (EPA) and social interest. The participants were 311 mothers with elementary schoolchildren who lived in the Daegu-Gyeongbuk area. Data were collected through an online questionnaire provided on the portal site and analyzed using k-means clustering, t-test, One-Way ANOVA, and Pearson's correlation using IBM SPSS Statistics 21 for Windows and, RMSEA, TLI, NFI and CFI using IBM SPSS AMOS 18 for Windows. The principal results were as follows. Firstly, mothers' EPA anxiety and avoidance had a negative influence on social interest. Secondly, social interest was found to be significantly higher among mothers with a secure attachment style than among mothers with an insecure attachment style. Thirdly, significant differences were observed in levels of social interest among mothers with secure, preoccupied, dismissive, and disorientated attachment styles. A Scheffé post-hoc test revealed that social interest was significantly higher among mothers with a secure attachment style than among mothers with a disorientated attachment style. The experience of relationships with caregivers early in life is therefore important in the development of social interest.