• Convergence Security Journal
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• v.12 no.3
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• pp.99-106
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• 2012

Today, the typical web hacking attacks are cross-site scripting(XSS) attacks, injection vulnerabilities, malicious file execution and insecure direct object reference included. Web hacking security systems, access control solutions, access only to the web service and flow inside but do not control the packet. So you have been illegally modified to pass the packet even if the packet is considered as a unnormal packet. The defense system is to fail to appropriate controls. Therefore, in order to ensure a successful web services diagnostic system development is necessary. Web application diagnostic system is real and urgent need and alternative. The diagnostic system development process mu st be carried out step of established diagnostic systems, diagnostic scoping web system vulnerabilities, web application, analysis, security vulnerability assessment and selecting items. And diagnostic system as required by the web system environment using tools, programming languages, interfaces, parameters must be set.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.91-97
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• 2020

Use of voice-based interfaces that can interact with users is increasing as AI technology develops. To date, however, most of the research on voice-based interfaces has been technical in nature, focused on areas such as improving the accuracy of speech recognition. Thus, the voice of most voice-based interfaces is uniform and does not provide users with differentiated sensibilities. The purpose of this study is to add a emotional factor suitable for the AI interface. To this end, we have derived emotional factors that should be considered in designing voice interface. In addition, we looked at voice trends that differed from country to country. For this study, we conducted interviews with voice industry experts from Finland and Norway, countries that use their own independent languages.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.31-36
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• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.365-370
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• 2021

The current study aims to investigate the relationship between working memory capacity, vocabulary size, and listening competence of Korean EFL (English as a Foreign Language) learners. 30 English education majors from a university in Korea were recruited. The backward digit span and the operation span tasks were used to measure the participants' working memory capacity, while the Listening Vocabulary Level Test (LVLT) and the Michigan English Test (MET) were employed to measure their vocabulary size and listening proficiency in English, respectively. The correlational analyses revealed that the bigger one's working memory storage was, the better the person processed incoming input. However, no statistically significant correlation was found between working memory capacity, English vocabulary size, and listening proficiency, possibly due to the small sample size and the homogeneous subjects.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.3
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• pp.747-756
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• 2019

Students studying interpretation perform additional study and training in addition to regular class. In simultaneous interpreting and consecutive interpreting, interpreter should memorize speaker's announcement because of different language structure. To improve short-term memory, students perform memory training that requires a pair of students. Therefore, they can not perform self-learning, and therefore, efficiency of studying decreases. To resolve this problem, computer based short-term memory training system has been proposed. Student can perform self-learning by changing words in text to special character in the training system. However, efficiency of studying decreases because computer has low portability. Since the number of words is larger than the number of words to be switched into special character, learning difficulty decreases. To resolve this problem, smart device based short-term memory training system has been proposed. Student can perform smart device based training system without space constraints. Since the proposed training system increases the number of words to be changed into special character, learning difficulty increases. We implemented and evaluated the functionalities of the proposed training system.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.248-253
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• 2019

Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.

• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.299-304
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• 2019

Recently, the development of rehabilitation medical technology has resulted in an increased interest in speech therapy equipment. In particular, research on articulation therapy for communication disorders is being actively conducted. Existing methods for the diagnosis and treatment of speech disorders have many limitations, such as traditional tactile perception tests and methods based on empirical judgment of speech therapists. Moreover, the position and tension of the tongue are key factors of speech disorders with regards to articulation. This is a very important factor in the distinction of Korean characters such as lax, fortis, and aspirated consonants. In this study, we proposed a Korean electropalatography (EPG) system to easily measure and monitor the position and tension of the tongue in articulation treatment and diagnosis. In the proposed EPG system, a sensor was fabricated using an AgCl electrode and biocompatible silicon. Furthermore, the measured signal was analyzed by implementing the bio-signal processing module and monitoring program. In particular, the bio-signal was measured by inserting it into the palatal from an experimental control group. As a result, it was confirmed that it could be applied to clinical treatment in speech therapy.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.237-243
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• 2019

This study used latent profile analysis to identify heterogeneous subgroups with respect to the perception of work environment among workers in residential facilities for people with disabilities. The final model yielded five latent profiles of the perception of work environment: 'low workload group' who scored low in time pressure and job responsibility, 'discrimination perception group' who scored low in supervisor support and high in sex and academic/regional discriminations, 'positive perception group' who scored high in work autonomy and peer and supervisor supports, 'high workload group' who scored high in workload, time pressure, and job responsibility, and 'average group' who scored average in most areas. In the case of job satisfaction, positive perception group was the highest, followed by average group, low and high workload groups, and discrimination perception group.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.193-198
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• 2019

This study analyzed the changes of vowel formant, voice intensity, and fundamental frequency of vowels for 11 months using acoustochemical spectrogram analysis of women diagnosed with amyotrophic lateral sclerosis (ALS). The test word was a vowel /a, i, u/ and a diphthong /h + ja + da/, /h + wi + da/, and /h +ɰi+ da/. Speech data were collected through the word reading task presented on the monitor using 'Alvin' program, and the recording environment was set to 5,500 Hz for the nyquist frequency and 11,000 Hz for the sampling rate. The records were analyzed by using spectrograms to vowel formants, voice intensity, and fundamental frequency. As a result of analysis, the fundamental frequency and intensity of the ALS process were decreased and the formant slope of the diphthong was decreased rather than the formant change in the vowel. This result suggests that the vowel distortion of ALS due to disease progression is due to the decrease of tongue and jaw co morbidity.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.