• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.11
• /
• pp.6877-6883
• /
• 2014

The continuous social development has led to increasing pollution in lakes. This study proposed the LVRI (Lake Vulnerability Resilience Indicator) based on the vulnerability assessment of climate change for an environmental risk assessment in lakes sufferign water pollution in an integrated aspect of the characteristics in lake watersheds. A total of 11 representative assessment factors were selected and constructed for 6 lake basins in the Geum River Watershed to calculate the exposure, sensitivity and adaptation indicators in a vulnerability assessment classification system. The weight coefficients for assessment factors of the LVRI were also calculated using the Entropy method. This study also compared the rank results of the lake environmental risk with/without the weight coefficients of assessment factors for the practical application of the proposed lake environmental risk assessment method. The lake environmental risk results estimated in this study can be used for long-term water quality analysis and management in lakes.

• Journal of Biomedical Engineering Research
• /
• v.33 no.3
• /
• pp.141-147
• /
• 2012

In this paper, we have investigated effects of two specific meditations (Chinese qigong meditation and Kundalini yoga meditation) on the heart rate variability (HRV), which is a well-known quantitative measure of autonomic balance, of skilled students. To analyze the effects, the MIT/BIH physionet database was utilized. The database includes RR intervals of eight skilled Chinese qigong meditators (5 women and 3 men; age range 26-35) and four skilled Kundalini yoga meditators (2 women and 2 men; age range 20-52). RR intervals of each subject were measured before and during the meditations. For HRV analysis, we have used typical four HRV parameters - the low frequency to high frequency power ratio (LF/HF ratio), SD2/SD1 ratio, sample entropy, and fractal dimension. The LF/HF ratio was calculated by the autoregressive spectrum and the SD2/SD1 ratio was derived from the Poincar$\grave{e}$ plot. The sample entropy was computed from the phase space plot and the fractal dimension was estimated by the Higuchi's algorithm. In the experiments, the Wilcoxon signed rank test was employed because we used small datasets and compared HRV parameters before and during the meditations. As a result, we have found increment of the LF/HF and SD2/SD1 ratios in both meditations; whereas the sample entropy is decreased during the meditations. In addition, the fractal dimension is increased during the Chinese qigong meditation; whereas it is decreased during the Kundalini yoga meditation. The results show that the sympathetic nervous system is generally more activated in skilled Chinese qigong and Kundalini yoga meditators, but the activation of the parasympathetic nervous tone is suppressed.

• The KIPS Transactions:PartB
• /
• v.16B no.5
• /
• pp.377-384
• /
• 2009

A new algorithm in order to classify various contents in the image documents, such as text, figure, graph, table, etc. is proposed in this paper by classifying contents using texture-based PCA, and by segmenting document images using local entropy-based histogram. Local entropy and histogram made the binarization of image document not only robust to various transformation and noise, but also easy and less time-consuming. And texture-based PCA algorithm for each segmented region was taken notice of each content in the image documents having different texture information. Through this, it was not necessary to establish any pre-defined structural information, and advantages were found from the fact of fast and efficient classification. The result demonstrated that the proposed method had shown better performances of segmentation and classification for various images, and is also found superior to previous methods by its efficiency.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
• /
• 2003.09a
• /
• pp.574-578
• /
• 2003

There are so many methods to estimate the groundwater recharge. These methods can be categorized into four groups. First groupis related to the water balance analysis, second group is concerned with baseflow/springflow recession, and third group is interested in some types of tracers; environmental tracers and/or temperature profile. The limitation of these types of methods is that the estimated results of recharge are presented in the form of an average over some time period. Forth group has a little different approach. They use the time series data of hydraulic head and specific yield evaluated from field test, and the results of estimation are described in the sequential form. But their approach has a serious problem. The estimated results in forth typeof methods are generally underestimated because they cannot consider the discharge phase of water table fluctuation coupled with the recharge phase. Ketchum el. at. (2000) proposed calibrated method, considering recharge- and discharge-coupled water table fluctuation. But the dischargeis considered just as the areal average with discharge rate. On the other hand, there are many methods to estimate the source wavelet with observed data set in geophysics/signal processing and geophysical methods are rarely applied to the estimation of groundwater recharge. The purpose this study is the evaluation of the applicability of one of the geophysical method in the estimation of sequential recharge rate. The applied geophysical method is called minimum entropy deconvolution (MED). For this purpose, numerical modeling with linearized Boussinesq equation was applied. Using the synthesized hydraulic head through the numerical modeling, the relative sequenceof recharge is calculated inversely. Estimated results are very concordant with the applied recharge sequence. Cross-correlations between applied recharge sequence and the estimated results are above 0.985 in all study cases. Through the numerical test, the availability of MED in the estimation of the recharge sequence to groundwater was investigated

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.8 no.1
• /
• pp.31-37
• /
• 2008

In this paper, we propose an entropy-based routing protocol to effectively support both stable route construction and route lifetime in Mobile Ad-hoc Wireless Sensor Networks (MAWSN). The basic idea and feature of the proposed routing protocol are as follows. First, we construct the stable routing routes based on entropy concept using mobility of mobile nodes. Second, we consider a realistic approach, in the points of view of the MAWSN, based on mobile sensor nodes as well as fixed sensor nodes in sensor fields while the conventional research for sensor networks focus on mainly fixed sensor nodes. The performance evaluation of the proposed routing protocol is performed via simulation using OPNET(Optimized Network Engineering Tool) and analysis. The results of the performance evaluation show that the proposed routing protocol can efficiently support both the construction of stable route and route lifetime in mobile ad-hoc wireless networks.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.6
• /
• pp.106-113
• /
• 2008

In this paper, we propose an Entropy-based Cooperative-Aided Routing Protocol (ECARP) in Mobile Ad-hoc fireless Sensor Networks (MAWSN). The main contributions and features of this paper are as follows. First, the entropy-based cooperative routing protocol which is based on node mobility is proposed for supporting stable routing route construction. Second, cooperative data transmission method is used for improving data transmission ratio with the improved SNR. Third, we consider a realistic approach, in the points of view of the MAWSN, based on mobile sensor nodes as well as fixed sensor nodes in sensor fields while the conventional research for sensor networks focus on mainly fixed sensor nodes. The performance evaluation of the proposed routing protocol is performed via simulation and analysis.

• Journal of KIISE:Software and Applications
• /
• v.32 no.5
• /
• pp.385-395
• /
• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.

• Korean Journal of Remote Sensing
• /
• v.36 no.1
• /
• pp.67-81
• /
• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• The Korean Journal of Applied Statistics
• /
• v.26 no.1
• /
• pp.81-92
• /
• 2013

It is important to construct a firm basis for accusing potential violators of academic integrity in order to avoid spurious accusations and false convictions. Educational researchers have developed many statistical methods that can either uncover or confirm cases of cheating on tests. However, most of them rely on simple correlation-based measures, and often fail to account for patterns in responses or answers. In this paper, we propose a new statistical index denoted by a Standardized Signed Entropy Similarity Score to resolve this difficulty. In addition, we apply the proposed method to analyze a real data set and compare the results to other existing methods.

• The KIPS Transactions:PartB
• /
• v.15B no.1
• /
• pp.61-68
• /
• 2008

This paper presents a spam filter system using co-training with spamminess features and URL features based on the maximum entropy model. Spamminess features are the emphasizing patterns or abnormal patterns in spam messages used by spammers to express their intention and to avoid being filtered by the spam filter system. Since spammers use URLs to give the details and make a change to the URL format not to be filtered by the black list, normal and abnormal URLs can be key features to detect the spam messages. Co-training with spamminess features and URL features uses two different features which are independent each other in training. The filter system can learn information from them independently. Experiment results on TREC spam test collection shows that the proposed approach achieves 9.1% improvement and 6.9% improvement in accuracy compared to the base system and bogo filter system, respectively. The result analysis shows that the proposed spamminess features and URL features are helpful. And an experiment result of the co-training shows that two feature sets are useful since the number of training documents are reduced while the accuracy is closed to the batch learning.