• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.595-603
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• 2002

Only a few studies have been conducted on how to select multiple classifiers from the pool of available classifiers for showing the good classification performance. Thus, the selection problem if classifiers on how to select or how many to select still remains an important research issue. In this paper, provided that the number of selected classifiers is constrained in advance, a variety of selection criteria are proposed and applied to tile construction of multiple classifier systems, and then these selection criteria will be evaluated by the performance of the constructed multiple classifier systems. All the possible sets of classifiers are trammed by the selection criteria, and some of these sets are selected as the candidates of multiple classifier systems. The multiple classifier system candidates were evaluated by the experiments recognizing unconstrained handwritten numerals obtained both from Concordia university and UCI machine learning repository. Among the selection criteria, particularly the multiple classifier system candidates by the information-theoretic selection criteria based on conditional entropy showed more promising results than those by the other selection criteria.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of KIISE:Information Networking
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• v.33 no.3
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• pp.201-217
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• 2006

In this paper, we collected the physical traces from high speed Internet backbone traffic and analyze the various characteristics of the underlying packet traces. Particularly, our work is focused on analyzing the characteristics of an anomalous traffic. It is found that in our data, the anomalous traffic is caused by UDP session traffic and we determined that it was one of the Denial of Service attacks. In this work, we adopted the unsupervised machine learning algorithm to classify the network flows. We apply the k-means clustering algorithm to train the learner. Via the Cramer-Yon-Misses test, we confirmed that the proposed classification method which is able to detect anomalous traffic within 1 second can accurately predict the class of a flow and can be effectively used in determining the anomalous flows.

• Fashion & Textile Research Journal
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• v.16 no.6
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• pp.927-934
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• 2014

Today Hanbok, the symbol of our nation's culture, has been preserved as cultural heritage despite natural and social environmental changes over years. This study is analyzing Hanbok which were taken in the National Skills Competition from 2000 to 2013; it presents our traditional stitching methods and clarifies the pride the Hanbok artisans have as inheriting the traditional heritage. Therefore this paper contributes to continuous and proper development of the culture of Hanbok and being loved by many people. The assignments about Hanbok featured in the National Skills Competition were categorized by national and local competition, time required to produce assignments, required amount of fabric, type of stitching, unlined and lined clothes depending on the type of assignment; according to the classification, methods for sewing were analyzed in detail. First, the national tournament presented 12 kinds of sewing techniques; especially hand-sewn stitching method appeared in common. Second, the unlined and lined clothes depending on the type of assignments had applied different sewing methods; double-stitching, flat fell seam, french seam, roll over sewing, blind stitch, right angle blind stitch, decorative saddle stitching, darning, etc. were used. Sewing methods in the 21st century has not changed over the years; the shape of clothes was changed upon trends. Sewing became easier than before by the invention of sewing machine, we should carry on our traditional sewing techniques. Through this analysis on sewing methods of the National Skills Competition, I hope to support competition participants for studying Hanbok and indicate proper sewing techniques.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.247-249
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• 2020

In the era of the 4th industrial revolution, the demand for convergence between ICT technologies is increasing in various fields. Accordingly, a new term that combines data, network, and artificial intelligence technology, DNA (Data, Network, AI) is in use. and has recently become a hot topic. DNA has various potential technology to be able to develop intelligent application in the real world. Therefore, this paper introduces the reviewed papers on the service image placement mechanism based on the logical fog network, the mobility support scheme based on machine learning for Industrial wireless sensor network, the prediction of the following BCI performance by means of spectral EEG characteristics, the warning classification method based on artificial neural network using topics of source code and natural language processing model for data visualization interaction with chatbot, related on DNA technology.

• Journal of IKEEE
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• v.8 no.2 s.15
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• pp.216-223
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• 2004

The customer's bulletin board is the important channel to get opinions from customers directly. The effective management of the bulletin board for the customer improves the reliance by providing the best replies and by accepting opinions of the customer and furthermore, that can raise the customer's reliance of the whole shopping mall is the important eCRM method. But, the present mostly customer's bulletin board is been replied without any classifying about many kinds of question. Consequently, The shopping mall should do systematic management of the best professional reply about many kinds of question. In order to resolve this problem, we implement a classifier called Naive Bayesian classifier is classified automatically bulletin board for eCRM of shopping mall.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.526-535
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• 2018

Many steganography algorithms have been studied, and steganalysis for detecting stego images which steganography is applied to has also been studied in parallel. Especially, in the case of the image steganalysis, the features such as ALE, SPAM, and SRMQ are extracted from the statistical characteristics of the image, and stego images are classified by learning the classifier using various machine learning algorithms. However, these studies did not consider the effect of image size, aspect ratio, or message-embedding rate, and thus the features might not function normally for images with conditions different from those used in the their studies. In this paper, we analyze the classification rate of the SPAM-based image stegnalysis against variety image sizes aspect ratios and message-embedding rates and verify its versatility.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1011-1019
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• 2015

CAPTCHA(Completely Automated Public Turing test to tell Computers and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most recent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from the background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHAs were correctly identified, it was demonstrated that the current CAPTCHA is not safe.