• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.29-38
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• 2005

Although people frequently rely on intuition in managing activities, they rarely use it in developing effective decision-making support systems. In this paper, we investigate and compare the correlations between such characteristics as cognition and emotion characteristics and judgmental time-series forecasting accuracy by using a self-organizing neural network, and eventually aim to help build efficient decision-making atmosphere. The neural network used in this paper employs a self-supervised adaptive algorithm, and the feature of which is that it inherently can use correlation between input vectors by exchanging information between neuron clusters in the self-organizing layer during the training. Our experiments showed that both cognition and emotion characteristics had correlations with judgmental time-series forecasting, and that cognition characteristics had larger correlation than emotion characteristics. We also found that conceptual style had larger correlation than behavioral and analytical styles, and displeasure-sleepiness style had larger correlation than pleasure-arousal style with the forecasting.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.316-320
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• 2015

In this paper, the pole filtering concept is applied to the Mel-frequency cepstral coefficient (MFCC) feature vectors in the conventional cepstral mean normalization (CMN) and cepstral mean and variance normalization (CMVN) frameworks. Additionally, performance of the cepstral mean and scale normalization (CMSN), which uses scale normalization instead of variance normalization, is evaluated in speech recognition experiments in noisy environments. Because CMN and CMVN are usually performed on a per-utterance basis, in case of short utterance, they have a problem that reliable estimation of the mean and variance is not guaranteed. However, by applying the pole filtering and scale normalization techniques to the feature normalization process, this problem can be relieved. Experimental results using Aurora 2 database (DB) show that feature normalization method combining the pole-filtering and scale normalization yields the best improvements.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.146-150
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• 1999

On the advent of digital era, ECDIS has emerged as a new navigation aid that should result in significant benefits to safe navigation. More than simply a graphics display, ECDIS is a new concept navigation system capable of providing integrated information of geographical and texual data. As an official vector data for ECDIS, ENC consists of spatial and feature data to describe objects in form of points, lines and areas. IHO published International Standards for ENC, such as S-52(Specification for Chart Content and Display Aspects of ECDIS and S-57(IHO Transfer Standard for Digital Hydrographic Data). This paper deals with the implementation of an EUC representation system which meets S-52 presentation specification and S-57 transfer standards by analyzing S-57 data structures and converting then to an appropriate internal data structures and representing them onto screen adopting S-52 presentation specification.

• Journal of KIISE
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• v.42 no.8
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• pp.1031-1038
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• 2015

The recent emergence of smart phones, wearable devices, and even the IoT concept made it possible for various objects to interact one another anytime and anywhere. Among many of such smart services, a smart home service typically requires a large number of sensors to recognize the residents' activities. For this reason, the ideas on activity recognition using the data obtained from those sensors are actively discussed and studied these days. Furthermore, plenty of sensors are installed in order to recognize activities and analyze their patterns via data mining techniques. However, if many of these sensors should be installed for IoT smart home service, it raises the issue of cost and energy consumption. In this paper, we proposed a new method for reducing the number of sensors for activity recognition in a smart environment, which utilizes the principal component analysis and clustering techniques, and also show the effect of improvement in terms of the activity recognition by the proposed method.

• International Area Studies Review
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• v.15 no.3
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• pp.175-188
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• 2011

This study aims to examine how the U.S. economic shocks affect the Japanese economy. It is widely believed that the U.S. economy has a significant effect on the Japanese economy. Actually, the U.S. accounts for a considerable amount of Japan's exports and imports. To the economic policymakers, it is very important to know how economic disturbances generated by the U.S. are transmitted to the Japanese economy. A vector autoregression(VAR) model is employed to investigate the international transmission channel of economic disturbances. The interactions of the U.S.-Japansese economy are investigated by using variance decompositions(VDCs). The results of this study provided the evidence that the U.S. economic shocks were important for the Japanese economy during the sample period. This study supports the notion of economic dependence of smaller open economy such as Japan as compared with larger economy such as the U.S.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.105-111
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• 2024

In this paper, we propose methods for the named entity normalization of traditional herbal formula found in medical texts. Specifically, we developed methodologies to determine whether mentions, such as full names of herbal formula and their abbreviations, refer to the same concept. Two different approaches were attempted. First, we built a supervised classification model that uses BERT-based contextual vectors and character similarity features of herbal formula mentions in medical texts to determine whether two mentions are identical. Second, we applied a prompt-based querying method using GPT-4o mini and GPT-4o to perform the same task. Both methods achieved over 0.9 in Precision, Recall, and F1-score, with the GPT-4o-based approach demonstrating the highest Precision and F1-Score. The results of this study demonstrate the effectiveness of machine learning-based approaches for named entity normalization in traditional medicine texts, with the GPT-4o-based method showing superior performance. This suggests its potential as a valuable foundation for the development of intelligent information extraction systems in the traditional medicine domain.

• Korean Journal of Ecology and Environment
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• v.52 no.4
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• pp.285-292
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• 2019

Our study was based on the long-term surveys with respect to the major reservoirs located in the Yeongsan and Seomjin river basins. A total of 45 survey sites have been surveyed four times a year from 2008 to 2017. We identified 166 zooplankton species, including 127 rotifers, 26 cladocerans, and 13 copepods. Mean population density and species number of small reservoirs were higher than those of mid and large reservoirs. Considering outliers exceeding the 90th percentile between species occupancy and mean abundance, 10 of 11 habitat generalists were rotifers, and Bosmina longirostris was the only cladoceran. Habitat specialist consisted of three species of rotifers and emerged from one to three survey sites. According to the modularity results, it was found that the survey sites covering the entire river basins were characterized into five groups, which was similar to the classification by maximum water surface areas(MWSA). The result of the eigenvector centrality showed that the size of MWSA had a greater impact on the similarity of zooplankton community structure between reservoirs than the difference in distance between reservoirs. In the case of survey points in near dam or estuary bank of Juam and Youngsan reservoirs, modularity class were separated from other internal survey points of those. Given that the zooplankton interactions may contribute to freshwater functions more than species diversity. These topological features provide new insight into studying zooplankton distribution patterns, their organization and impacts on freshwater-associated function.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.167-179
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• 2002

In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs) in order to compensate the weakness of HMM assumptions. The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function because the single frame feature cannot represent the temporal dynamics of speech signals effectively. To apply the segmental features to pattern classification, we adopted segmental HMM(SHMM) which is known as the effective method to represent the trend of speech signals. SHMM separates observation probability of the given state into extra- and intra-segmental variations that show the long-term and short-term variabilities, respectively. To consider the segmental characteristics in acoustic model, we present segmental-feature HMM(SFHMM) by modifying the SHMM. The SFHMM therefore represents the external- and internal-variation as the observation probability of the trajectory in a given state and trajectory estimation error for the given segment, respectively. We conducted several experiments on the TIMIT database to establish the effectiveness of the proposed method and the characteristics of the segmental features. From the experimental results, we conclude that the proposed method is valuable, if its number of parameters is greater than that of conventional HMM, in the flexible and informative feature representation and the performance improvement.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.399-406
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• 2002

The goal of this research has been to develop an adaptive user agent for web surfing. To achieve this goal, the research has concentrated on three issues: collection of user data, construction and improvement of user profile, and adaptation by applying the user profile. The main outcome from the research is a prototype system that provides the functional definition and componential design scheme for an adaptive user agent for the web environment. Internally, the system achieves its operational goal from the cooperation of two independent agents. They are IIA (Interactive Interface Agent) and UPA (User Profiling Agent). As a tool for providing a user-friendly interface environment, the IIA employs the Keyword Index, which is a list of index terms of a webpage as well as a keyword menu for subsequent queries, and the Suggest Link, which is a hierarchical list of URLs showing the past browsing procedure of the user. The UPA reflects in the User Profile, both the static and the dynamic information obtained from the user's browsing behavior. In particular, a user's interests are represented in the form of Interest Vectors which, based on the similarity of the vectors, is subject to update and creation, thus dynamically profiling the user's ever-shifting interests.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.2 s.14
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• pp.123-132
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• 1999

With the rapid increases of the amount of data and computing, the parallelization of the computing algorithm becomes necessary more than ever. However the parallelization had been conducted mostly in a super-computer until the rod 1990s, it was not for the general users due to the high price, the complexity of usage, and etc. A new concept for the parallel processing has been emerged in the form of K-clustering form the late 1990s, it becomes an excellent alternative for the applications need high computer power with a relative low cost although the installation and the usage are still difficult to the general users. The mapping algorithms (cut, join, resizing, warping, conversion from raster to vector and vice versa, etc) in GIS are well suited for the parallelization due to the characteristics of the data structure. If those algorithms are manipulated using PC-clustering, the result will be satisfiable in terms of cost and performance since they are processed in real flu with a low cos4 In this paper the tools and the libraries for the parallel processing and PC-clustering we introduced and how those tools and libraries are applied to mapping algorithms in GIS are showed. Parallel programs are developed for the mapping algorithms and the result of the experiments shows that the performance in most algorithms increases almost linearly according to the number of node.