• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4518-4540
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• 2019

With the development of IT convergence technology, autonomous driving has gradually developed; however, the vehicle is still operated by the driver, who should always be in good health - but sometimes, this is not the case. It is especially dangerous to drive when drowsy, and unable to fully concentrate on driving, such as when taking certain medicines, or through fatigue. Drowsy driving is at least eight times more dangerous than normal driving, and as dangerous as drunk driving. Previous research has looked at technology to detect drowsiness, in order to wake up drivers when necessary, or to safely stop the vehicle. Furthermore, many studies have been conducted to find out when drowsiness occurs. However, it is more desirable for the driver to take sufficient rest during a break, in order to be able to continue to focus and drive. In other words, it is important to maintain a normal state before drowsiness. In this study, we introduce a sound source to increase driver concentration and prevent drowsiness, another that can improve the quality of sleep, and a system that produces these sound sources. The proposed system has a noise reduction effect of about 15 dB. We have confirmed that the proposed sound induces an EEG of the desired form.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.229-237
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• 2021

Our modern 'information-hungry' age demands delivery of information at unprecedented fast rates. Timely delivery of noteworthy information about recent events can help people from different segments of life in number of ways. As world has become global village, the flow of news in terms of volume and speed demands involvement of machines to help humans to handle the enormous data. News are presented to public in forms of video, audio, image and text. News text available on internet is a source of knowledge for billions of internet users. Urdu language is spoken and understood by millions of people from Indian subcontinent. Availability of online Urdu news enable this branch of humanity to improve their understandings of the world and make their decisions. This paper uses available online Urdu news data to train machines to automatically categorize provided news. Various machine learning algorithms were used on news headline for training purpose and the results demonstrate that Bernoulli Naïve Bayes (Bernoulli NB) and Multinomial Naïve Bayes (Multinomial NB) algorithm outperformed other algorithms in terms of all performance parameters. The maximum level of accuracy achieved for the dataset was 94.278% by multinomial NB classifier followed by Bernoulli NB classifier with accuracy of 94.274% when Urdu stop words were removed from dataset. The results suggest that short text of headlines of news can be used as an input for text categorization process.

• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Korean Institute of Interior Design Journal
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• v.20 no.4
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• pp.174-182
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• 2011

This study analyzed visitors' behaviors in the viewpoint of Attraction Power and Holding Power of exhibits on the basis of exhibition layout of real science museums. Through the analysis, the study grasped efficiency of analysis index and exhibition environment elements which might have an effect on planning the exhibition space of a large-scale museum and producing detailed ranges of exhibition. The main indicators used are: 1. Attraction Power: it indicates the relative incidence of people who have stopped in front of an object/exhibit during the exhibition tour. It is calculated by dividing the number of people who stop by the total number of people who have visited the museum or gallery. 2. Holding Power: it measures the average time spent in front of an information/communication element. It is calculated by dividing the average time of stay by the time "necessary" to read an element. As a result of analyzing the exhibition areas of National Science Museum (Daejeon) and National Museum of Emerging Science and Innovation(Tokyo), the Holding Power was found to be relatively lower than the Attracting Power. This means that 3.5 out of 10 visitors stop in front of the exhibit in 6 exhibition areas, and among these, only 1/10 is used when compared to the user required time of the exhibits. In other words, like the method of deriving an analysis index, the stage of viewing can be categorized as Attracting Power and Holding Power, and because the stage from Attracting Power to the stage of Holding Power are strongly linked, it shows that it is not easy to display a meaningful result. Except, the general distribution of Attracting Power was shown to be high from the entrance area of the exhibition hall based on the standard of viewing sequence. Also, the Holding Power became sequentially lower according to the sequence of exhibition viewing and displayed a meaningful interrelationship with the distribution ratio of island exhibits. In the case of island exhibition method, it is less influenced by the movement flow of visitors when compared to the wall type method of exhibition and can be understood as an exhibition method that provides spatial chances enabling stopping and viewing.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.455-464
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• 2009

Based on the observation that Korean speakers of English have difficulties in producing English fricatives, a perception experiment was designed to investigate whether Korean speakers also have difficulties perceiving English labial consonants including fricatives. Forty Korean college students were asked to perform a multiple-choice identification test. The consonant perception test consisted of nonce words which contained English labial consonants [p, b, f, v] in 4 different prosodic locations: initial onset position, intervocalic position before stress, intervocalic position after stress, and final coda position. The general perception pattern was that the mean accuracy rates were higher in strong position like CV and VCVV than in weak position like VC and VVCV. The difficulties in perceiving the English targets resulted mainly from bidirectional manner confusion between stop and fricative across all prosodic locations. The other types of misidentification were due to place confusion as well as voicing confusion. Place confusion was generated mostly by the target [f] in all prosodic position due to acoustic properties. Voicing confusion was heavily influenced by prosodic position. The misperception of the participants was accounted for by phonetic properties and/or the participants' native language properties.

• Fashion & Textile Research Journal
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• v.18 no.1
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• pp.48-62
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• 2016

Consumers have come to demand high quality, affordable prices, and innovative product designs of the outdoor wear market due to their well-being and leisure oriented lifestyle. A new system of business in outdoor wear has emerged in the process through which corporations have endeavored to satisfy such consumer needs. Outdoor wear brands have utilized social network services (SNS) such as Facebook and Twitter as means of marketing and have built close relations with consumers based on communication through these media. Recently, explosively escalating SNS data are referred to as social big data, and now that every consumer online is a commentator, reviewer, and publisher, the outdoor wear market and all of its brands have to stop talking and start listening to how they are perceived. Therefore, this study employs Social $Metrics^{TM}$, a social big data analysis solution by Daumsoft, Inc., to verify changes in the allusions related to outdoor wear market found on SNS. This study aims to identify changes in consumer perceptions of outdoor wear based on changes in outdoor wear search words and trends in positive and negative public opinion found in SNS social big data. In addition, products of interest, the major brands mentioned, the attributes taken into consideration during purchases of products, and consumers' psychology were categorized and analyzed by means of keywords related to outdoor wear brands found on SNS. The results of this study will provide fundamental resources for outdoor wear brands' market entry and brand strategy implementation in the future.

• Speech Sciences
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• v.1
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• pp.237-259
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• 1997

Cleft Palate speech appears to have hyper/hyponasality with velopharyngeal insufficiency and articulation disorders. Previous studies on Cleft Palate speech have shown that speech tends to have lower airflow and air pressure. To examine the aerodynamic characteristics of Cleft Palate speech, Aerophone II Voice function Analyzer was used. We measured sound pressure level, airflow, air pressure and glottal power. Three Cleft Palate adults and five normal adults participated in this experiment. The test words are composed of: (1) the sustained vowel /o/ (2) /CiCi/, where C is one of three different stop consonants in Korean (3) /bimi/. Subjects were asked to produce /bimi/ five times without opening their lips. All the data was statistically tested by t-test for Cleft Palate patients before operation groups and control groups and paired t-test for Cleft Palate patients before and after operation groups. The results were as follow: (1) Cleft Palate patients generally speak with incomplete oral closure and lower oral air pressure. As a result, the SPL of Cleft Palate before operation is 3 dB lower than control groups. (2) Airflow of Cleft Palate in phonation and articulation is lower than that of control groups. However, it increased after operation. Lung volume and mean airflow in phonation are significantly increased (p<0.05). (3) Although velopharyngeal function (velar opening rate) of Cleft Palate is poor in comparison with control groups, it was recovered after operation. In this event maximum flow rate and mean airflow rate are significantly increased (p<0.05). (4) Air pressure of Cleft Palate in speech is lower than that of control groups. In general, the air pressure of Cleft Palate increased after operation. In this event air pressure of glottalized consonant is significantly increased (p<0.04). (5) Glottal Power(mean power, mean efficient and mean resistant) of Cleft Palate patients is lower than that of control groups. But mean efficient and mean resistant of Cleft Palate patients increased significantly (p<0.05) after operation.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.122-124
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• 2003

In recent, the various electric & electronic machines are newly developed everyday and the electricity supply system & environment on the process from generation to consumption of electricity also is being changed. In other words, both supplier and consumer of electricity are required to be responsible for their interruption costs. So, it is very important and meaningful work for evaluating the interruption costs in quantitative. Additionally, since the restructuring of electric industry is on going in world wide, after restructuring, most of all electric utilities and consumer have to consider the supply reliability and quality as a important element of the calculating the related costs and contract because it takes costs to keep the supply reliability and quality highly. Especially, the interruption or the supply reliability will have influence on the bilateral contract between supplier and customer as a key point to determine the once in competitive electric market. Therefore, it has very important moaning to calculate the interruption costs in the present that it is prepared to open the competitive electric market. In this paper, international standards, i.e. IEC, IEEE, are applied to the analysis on the interruption costs used in the questionnaires which are newly designed including short duration interruption by the authors instead of traditional interruption criteria. Firstly, using the questionnaires, the authors got related data from industries according to the standard industry classification which are being used in electric utility and other national statistics in Korea. However, analysis results are hard to say typical value because of the not so many samples. So, the authors are going to survey and focus on not all kinds of industry but a few kinds of them that their facilities are effected or stop by short duration interruptions, so there are large economical damages. Finally, the authors hope to find the reliable and meaningful model in interruption costs of industrial customer.