• Korean Journal of Ecology and Environment
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• v.39 no.2 s.116
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• pp.198-208
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• 2006

This research was to apply a multi-metric approach, so called the Index of Biological Integrity (IBI) as a tool for biological evaluations of water environments, to a wadable stream. For the study, we surveyed 5 sampling locations in Kap Stream during August 2004 ${\sim}$ September 2005. We also compared the biological data with long-term water quality data, obtained from the Ministry of Environment, Korea and physical habitat conditions based on the Quantitative Habitat Evaluation Index (QHEI). We used ten metric systems for the IBI model to evaluate biological stream health. Overall IBI values in Kap Stream averaged 24 (range: 20${\sim}$30, n=5), indicating a "fair ${\sim}$ poor" conditions according to the modified criteria of Karr (1981) and US EPA(1993). Exclusive of 4th survey, average IBI values at the upstream reach (S1 ${\sim}$ S3)and downstream reach (S4 ${\sim}$ S5) were 20 and 24, respectively. However, in 4th survey the averages were 21 and 20 in the upstream and downstream reaches, respectively. This difference was larger in the upstream than in the downstream because of physical condition disturbed during summer monsoon. Values of the QHEI varied from 75(fair condition) to 148 (good condition) and values of QHEI in the S3 were significantly (P=0.001, n=5) lower than other sites. Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) were greater by 3 ${\sim}$ 8 fold in the downstream than in the upstream reach. We believe that present IBI approach applied in this study may be used as a key tool to set up specific goals for restoration of Kap Stream.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.171.2-171.2
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• 2016

The 2-dimensiona electron gas (2DEG) layers have opened tremendous interests in the heterooxide interfaces formed between two insulating materials, especially between LaAlO3 and $SrTiO_3$. The 2DEG layers exhibit extremely high mobility and carrier concentrations along with metallic transport phenomena unlike the constituent oxide materials, i.e., $LaAlO_3$ and $SrTiO_3$. The current work inserted artificially the interfacial layer, $Sr_xCa_{1-x}TiO_3$ between $LaAlO_3$ and $SrTiO_3$, with the aim to controlling the 2-dimensional transports. The insertion of the additional materials affect significantly their corresponding electrical transports. Such features have been probed using DC and AC-based characterizations. In particular, impedance spectroscopy was employed as an AC-based characterization tool. Frequency-dependent impedance spectroscopy have been widely applied to a number of electroceramic materials, such as varistors, MLCCs, solid electrolytes, etc. Impedance spectroscopy provides powerful information on the materials system: i) the simultaneous measurement of conductivity and dielectric constants, ii) systematic identification of electrical origins among bulk-, grain boundary-, and electrode-based responses, and iii) the numerical estimation on the uniformity of the electrical origins. Impedance spectroscopy was applied to the $LaAlO_3/Sr_xCa_{1-x}TiO_3/SrTiO_3$ system, in order to understand the 2-dimensional transports in terms of the interfacial design concepts. The 2-dimensional conduction behavior system is analyzed with special emphasis on the underlying mechanisms. Such approach is discussed towards rational optimization of the 2-dimensional nanoelectronic devices.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.383-394
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• 2018

In this paper, we investigate image and video compression techniques based on deep learning which are actively studied recently. The deep learning based image compression technique inputs an image to be compressed in the deep neural network and extracts the latent vector recurrently or all at once and encodes it. In order to increase the image compression efficiency, the neural network is learned so that the encoded latent vector can be expressed with fewer bits while the quality of the reconstructed image is enhanced. These techniques can produce images of superior quality, especially at low bit rates compared to conventional image compression techniques. On the other hand, deep learning based video compression technology takes an approach to improve performance of the coding tools employed for existing video codecs rather than directly input and process the video to be compressed. The deep neural network technologies introduced in this paper replace the in-loop filter of the latest video codec or are used as an additional post-processing filter to improve the compression efficiency by improving the quality of the reconstructed image. Likewise, deep neural network techniques applied to intra prediction and encoding are used together with the existing intra prediction tool to improve the compression efficiency by increasing the prediction accuracy or adding a new intra coding process.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8371-8376
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• 2014

Background: The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and Methods: We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model. Results: We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained. Conclusions: These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.29-48
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• 2012

As the complexity and uncertainty of social and economic systems increase, the strategic foresight that actively and effectively responds to the environmental changes becomes important. A wide range of future forecasting methods are available for strategic foresight. Selecting one of the methods depends on several factors such as availability of time and financial resources and the objectives of the exercise. Although trend extrapolation analysis has been used for many years, scenario planning is being widely used by government and corporate as a tool for strategic decision making in recent years. Generally, scenario planning is carried out through workshop, in which experts with diverse backgrounds exchange information, views, and insights and integrate the diverse viewpoints. However, only a small number of experts can participate in a workshop and citizen opinion is not easily transformed into the policy for the scenario exercise due to the limitation of budget and short duration of a project. It is also much harder to develop creative ideas in the workshop because of the limited time and space. In this study, a new scenario process combining scenario workshop and wiki is proposed to overcome the limitation of scenario workshop. This combined approach can be more productive than using scenario workshop alone when developing new ideas. In this study, we applied the combined approach to develop scenarios for the strategic foresight of future media and present suggestions for improving the process.

• Journal of Korea Game Society
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• v.7 no.2
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• pp.83-90
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• 2007

One result of the trend towards globalization is an increased number of projects that focus on natural language processing. Automatic speech recognition (ASR) technologies, for example, hold great promise in facilitating global communications and collaborations. Unfortunately, to date, most research projects focus on single widely spoken languages. Therefore, the cost to adapt a particular ASR tool for use with other languages is often prohibitive. This work takes a more general approach. We propose an International Phoneticizing Engine (IPE) that interprets input files supplied in our Phonetic Language Identity (PLI) format to build a dictionary. IPE is language independent and rule based. It operates by decomposing the dictionary creation process into a set of well-defined steps. These steps reduce rule conflicts, allow for rule creation by people without linguistics training, and optimize run-time efficiency. Dictionaries created by the IPE can be used with the speech recognition system. IPE defines an easy-to-use systematic approach that can obtained 92.55% for the recognition rate of Korean speech and 89.93% for English.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.1-22
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• 2009

The World Wide Web is transitioning from being a mere collection of documents that contain useful information toward providing a collection of services that perform useful tasks. The emerging Web service technology has been envisioned as the next technological wave and is expected to play an important role in this recent transformation of the Web. By providing interoperable interface standards for application-to-application communication, Web services can be combined with component based software development to promote application interaction and integration both within and across enterprises. To make Web services for service-oriented computing operational, it is important that Web service repositories not only be well-structured but also provide efficient tools for developers to find reusable Web service components that meet their needs. As the potential of Web services for service-oriented computing is being widely recognized, the demand for effective Web service discovery mechanisms is concomitantly growing. A number of techniques for Web service discovery have been proposed, but the discovery challenge has not been satisfactorily addressed. Unfortunately, most existing solutions are either too rudimentary to be useful or too domain dependent to be generalizable. In this paper, we propose a Web service organizing framework that combines clustering techniques with string matching and leverages the semantics of the XML-based service specification in WSDL documents. We believe that this is one of the first attempts at applying data mining techniques in the Web service discovery domain. Our proposed approach has several appealing features : (1) It minimizes the requirement of prior knowledge from both service consumers and publishers; (2) It avoids exploiting domain dependent ontologies; and (3) It is able to visualize the semantic relationships among Web services. We have developed a prototype system based on the proposed framework using an unsupervised artificial neural network and empirically evaluated the proposed approach and tool using real Web service descriptions drawn from operational Web service registries. We report on some preliminary results demonstrating the efficacy of the proposed approach.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5A
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• pp.351-360
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• 2011

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for offshore structure, there are few cases to apply structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure under changes in temperature and transverse loadings. Principal component analysis (PCA) was applied using conventional damage index to eliminate principal components sensitive to environmental change. It was found that the proposed PCA-base approach is an effective tool for long-term SHM under significant environmental changes.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.569-579
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• 2013

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.115-126
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• 2012

Mashups have become very popular over the last few years, and their use also varies for IT convergency services. In spite of their popularity, there are several challenging issues when combining Open APIs into mashups, First, since portal sites may have a large number of APIs available for mashups, manually searching and finding compatible APIs can be a tedious and time-consuming task. Second, none of the existing portal sites provides a way to leverage semantic techniques that have been developed to assist users in locating and integrating APIs like those seen in traditional SOAP-based web services. Third, although suitable APIs have been discovered, the integration of these APIs is required for in-depth programming knowledge. To solve these issues, we first show that existing techniques and algorithms used for finding and matching SOAP-based web services can be reused, with only minor changes. Next, we show how the characteristics of APIs can be syntactically defined and semantically described, and how to use the syntactic and semantic descriptions to aid the easy discovery and composition of Open APIs. Finally, we propose a goal-directed interactive approach for the dynamic composition of APIs, where the final mashup is gradually generated by a forward chaining of APIs. At each step, a new API is added to the composition.