• Management & Information Systems Review
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• v.6
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• pp.63-88
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• 2001

This paper presents a case study regarding a simple accounting information analysis method through the practical design using spreadsheet tool-Excel, which can offer to the management with more useful and practical information for the management decision-making. In this study, spreadsheet program is used to constitute case of the accounting information analysis method. Spreadsheet tool-Excel is easy to analyze the accounting information. And it can constitute a necessary program through function menu. In conclusion, the spreadsheet program should be used for quantitative analysis and evaluation on the accounting information. And it should be used to perform management activities. The results of this study may be summarized as follows: First, it is possible to constitute useful and practical case in the accounting information analysis method with spreadsheet program. Second, this study proposes directions for the accounting information analysis method with spreadsheet program.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.83-93
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• 2001

We investigate a kernel approach to discriminant analysis for binary classification as a machine learning point of view. Our view of the kernel approach follows support vector method which is one of the most promising techniques in the area of machine learning. As usual discriminant analysis, the kernel method can discriminate an object most likely belongs to. Moreover, it has some advantage over discriminant analysis such as data compression and computing time.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.35-45
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• 2005

Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.514-525
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• 1996

In this paper, Push-push Osciplier(Oscillator + Multiplier) has been analyzed by even-odd mode analysis method. A 10GHz DRO, an Osciplier using 10GHz DRO design method and an Osciplier using even-odd mode analysis method were designed, fabricated and tested to verify this method. The measured results verified the validity of the analysis method using even-odd mode analysis.

• Journal of information and communication convergence engineering
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• v.17 no.4
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• pp.239-245
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• 2019

Recently, effort to obtain various information from the vast amount of social network services (SNS) big data generated in daily life has expanded. SNS big data comprise sentences classified as unstructured data, which complicates data processing. As the amount of processing increases, a rapid processing technique is required to extract valuable information from SNS big data. We herein propose a system that can extract human sentiment information from vast amounts of SNS unstructured big data using the naïve Bayes algorithm and natural language processing (NLP). Furthermore, we analyze the effectiveness of the proposed method through various experiments. Based on sentiment accuracy analysis, experimental results showed that the machine learning method using the naïve Bayes algorithm afforded a 63.5% accuracy, which was lower than that yielded by the NLP method. However, based on data processing speed analysis, the machine learning method by the naïve Bayes algorithm demonstrated a processing performance that was approximately 5.4 times higher than that by the NLP method.

• Journal of Communications and Networks
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• v.11 no.6
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• pp.556-563
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• 2009

A side channel analysis is a very efficient attack against small devices such as smart cards and wireless sensor nodes. In this paper, we propose an efficient key detection method using a peak selection algorithm in order to find the advanced encryption standard secret key from electromagnetic signals. The proposed method is applied to a correlation electromagnetic analysis (CEMA) attack against a wireless sensor node. Our approach results in increase in the correlation coefficient in comparison with the general CEMA. The experimental results show that the proposed method can efficiently and reliably uncover the entire 128-bit key with a small number of traces, whereas some extant methods can reveal only partial subkeys by using a large number of traces in the same conditions.

• Journal of the Korea Society of Computer and Information
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• v.24 no.6
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• pp.89-97
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• 2019

This study proposes a method for investigating current patents related to information communication technology and smart mobility to provide insights into future technology trends. The method is based on text mining clustering analysis. The method consists of two stages, which are data preparation and clustering analysis, respectively. In the first stage, tokenizing, filtering, stemming, and feature selection are implemented to transform the data into a usable format (structured data) and to extract useful information for the next stage. In the second stage, the structured data is partitioned into groups. The K-medoids algorithm is selected over the K-means algorithm for this analysis owing to its advantages in dealing with noise and outliers. The results of the analysis indicate that most current patents focus mainly on smart connectivity and smart guide systems, which play a major role in the development of smart mobility.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.344-352
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• 2023

The purpose of this study is to empirically grasp the relationship between website immersion and website attitude by the attribute factors of the Taekwondo information website and provide it as basic data for effective operation of the Taekwondo information website. The subjects of this study were Taekwondo athletes enrolled in high schools and universities affiliated with the Korean Taekwondo Association, and the sampling method was sampled using the convient sampling method, a non-probability sampling method. Of the 820 questionnaires finally obtained, 789 were processed using PASW Statistics 20.0 and AMOS, except for 31 that were deemed to have poor respondents' contents or were not valuable as data. For data analysis, the statistical analysis techniques used in this study were frequency analysis, factor analysis, Cronbach's α test, correlation analysis, and structural equation model analysis (SEM), and the significance level of the research hypothesis was α=.It was verified at 05. The following conclusions were drawn through such research methods and procedures. First, information, entertainment, structure, cognition, searchability, and connectivity of Taekwondo information website attributes affect website immersion. Second, website immersion is affecting website attitudes.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1243-1263
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• 2018

The two-stage linear discrimination analysis (TSLDA) is a feature extraction technique to solve the small size sample problem in the field of image recognition. The TSLDA has retained all subspace information of the between-class scatter and within-class scatter. However, the feature information in the four subspaces may not be entirely beneficial for classification, and the regularization procedure for eliminating singular metrics in TSLDA has higher time complexity. In order to address these drawbacks, this paper proposes an improved two-stage linear discriminant analysis (Improved TSLDA). The Improved TSLDA proposes a selection and compression method to extract superior feature information from the four subspaces to constitute optimal projection space, where it defines a single Fisher criterion to measure the importance of single feature vector. Meanwhile, Improved TSLDA also applies an approximation matrix method to eliminate the singular matrices and reduce its time complexity. This paper presents comparative experiments on five face databases and one handwritten digit database to validate the effectiveness of the Improved TSLDA.