• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.68-76
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• 2023

Nowadays, the power of data analytics in general and visual data analytics, in particular, have been proven to be an important area that would help development in any domain. Many well-known IT services best practices have touched on the importance of data analytics and visualization and what it can offer to information technology service management. Yet, little research exists that summarises what is already there and what can be done to utilise further the power of data analytics and visualization in this domain. This paper is divided into two main parts. First, a number of IT service management tools have been summarised with a focus on the data analytics and visualization features in each of them. Second, interviews with five senior IT managers have been conducted to further understand the usage of these features in their organisations and the barriers to fully benefit from them. It was found that the main barriers include a lack of good understanding of some visualization design principles, poor data quality, and limited application of the technology and shortage in data analytics and visualization expertise.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• International Journal of Contents
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• v.11 no.4
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• pp.44-55
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• 2015

There has been a significant paradigm shift in the book industry from print to digital, with the increased use of electronic books (e-books) on e-book readers. The major online booksellers and publishers are devoting their energies to the growth of the e-book market, resulting in an upward spiral in e-book usage, and a resultant increase in the number of downloaded e-books in an e-book reader library. However, there are comparatively few features for e-book management and search in most e-book reader libraries, particularly in smartphone environments. In addition, the user interfaces of e-book management in e-book readers are highly diverse, which has led to major usability issues. In this paper, we analyze user preferences for e-book management and search in the libraries of the five most commonly used e-readers for the Android smartphone platform via a questionnaire survey. Then, we suggest ideal alternatives in addition to user-friendly features based on user preferences for managing e-book libraries, to allow users to more easily browse collections, thereby enhancing the usability of e-book readers.

• Journal of Haehwa Medicine
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• v.19 no.2
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• pp.219-228
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• 2011

Objective : Sasang constitution medicine utilizes voice characteristics to diagnose a person's constitution. In this paper we propose methods to analyze Sasang constitution using speech information technology. That is, this study aims at establishing the relationship between Sasang constitutions and their corresponding voice characteristics by investigating various speech variables. Materials & Methods : Voice recordings of 1,406 speakers are obtained whose constitutions have been already diagnosed by the experts in the fields. A total of 144 speech features obtained from five vowels and a sentence are used. The features include pitch, intensity, formant, bandwidth, MDVP and MFCC related variables for each constitution. We analyze the speech variables and find whether there are statistically significant differences among three constitutions. Results : The main speech variables classifying three constitutions are related to pitch and MFCCs for male, and formant and MFCCs for female. The correct decision rate is 73.7% for male Soeumin, 63.3% for male Soyangin, 57.3% for male Taeumin, 74.0% for female Soeumin, 75.6% for female Soyangin, 94.3% for female Taeumin, and 73.0% on the average. Conclusion : Experimental results show that statistically significant correlation between some speech variables and the constitutions is observed.

• Korean Journal of Environmental Biology
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• v.23 no.3 s.59
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• pp.215-220
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• 2005

Through multiple stage analysis, the biochores in Korea were classified and organized as a hierarchy system. In the 1st step, the isopleth lines of warmth index $100^{\circ}C,\;85^{\circ}C,\;55^{\circ}C\;\cdot$ month and the coldness index $-8^{\circ}C,\;or-10^{\circ}C\;\cdot$ month, which indicate the boundaries of plant formation zones (Yim and Kira 1975; Yim 1977), were applied in the determination of major biochores. In the 2nd step, these biochores were subdivided into the five classes based on Thornthwaite's moisture index (Im) and Yim and Kira (1976), as follows: $100{\le}Im,100 In the 3 rd step, the analysis of topographic features yielded three categories of flatlands, gentle slope, and steep slope areas. These were obtained by adopting the $100{\times}100-meter$ gridded DEM and by considering the physical features of the Korean Peninsula. The features of relief in mountainous areas, waters, islands, etc. were converted into climatic indices. This grouping of biochores serves as a useful tool for the interpretation of the distributional patterns of vegetation of vascular plants and similar phenomena.

• Journal of Internet Computing and Services
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• v.9 no.1
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• pp.129-135
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• 2008

This paper presents a methodology to forecast KOSPI index by extracting fuzzy rules based on the neural network with weighted fuzzy membership functions (NEWFM) and the minimized number of input features using the distributed non-overlap area measurement method. NEWFM classifies upward and downward cases of KOSPI using the recent 32 days of CPPn,m (Current Price Position of day n for n-1 to n-m days) of KOSPI. The five most important input features among CPPn,m and 38 wavelet transformed coefficients produced by the recent 32 days of CPPn,m are selected by the non-overlap area distribution measurement method. For the data sets, from 1991 to 1998, the proposed method shows that the average of forecast rate is 67.62%.

• ETRI Journal
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• v.37 no.4
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• Journal of Digital Convergence
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• v.11 no.9
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• pp.301-310
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• 2013

The purpose of this study is to explore the meanings of the features of university students with regard to their smartphone usage in order to establish the culture of good smartphone use. In-depth interviews were conducted with nine university students. The collected data were analyzed qualitative method. As a results, five theme and 18 subthemes of characteristic of smartphone usage were identified. Those themes include 1. background of smartphone usage, 2. attachment to smartphone usage, 3. building of my own the world, 4. emergence of new human relation, 5. burden of smartphone usage. This study provided practical suggestions and follow studies for good smartphone usage.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.132-139
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• 2019

We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.

• Research in Mathematical Education
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• v.25 no.4
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• pp.285-309
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• 2022

A large body of previous studies investigated mathematical tasks by analyzing the design process prior to lessons or textbooks. While researchers have revealed the significant roles of mathematical tasks within written curricular, there has been a call for studies about how mathematical tasks are implemented or what is experienced and learned by students as enacted curriculum. This article proposes a mathematical task analytic framework based on a holistic definition of tasks encompassing both written tasks and the process of task enactment. We synthesized the features of the mathematical tasks and developed a task analytic framework with multiple dimensions: breadth, depth, bridging, openness, and interaction. We also applied the scoring rubric to analyze three multiplication tasks to illustrate the framework by its five dimensions. We illustrate how a series of tasks are analyzed through the framework when students are engaged in multiplicative thinking. The framework can provide important information about the qualities of planned tasks for mathematics instruction (proactive) and the qualities of implemented tasks during instruction (reactive). This framework will be beneficial for curriculum designers to design rich tasks with more careful consideration of how each feature of the tasks would be attained and for teachers to transform mathematical tasks with the provision of meaningful learning activities into implementation.