• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.175-192
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• 2022

Artificial intelligence-based natural language processing technology is playing an important role in helping users write English-language documents. For academic documents in particular, the English proofreading services should reflect the academic characteristics using formal style and technical terms. But the services usually does not because they are based on general English sentences. In addition, since existing studies are mainly for improving the grammatical completeness, there is a limit of fluency improvement. This study proposes an automatic academic English editing methodology to deliver the clear meaning of sentences based on the use of technical terms. The proposed methodology consists of two phases: misspell correction and fluency improvement. In the first phase, appropriate corrective words are provided according to the input typo and contexts. In the second phase, the fluency of the sentence is improved based on the automatic post-editing model of the bidirectional recurrent neural network that can learn from the pair of the original sentence and the edited sentence. Experiments were performed with actual English editing data, and the superiority of the proposed methodology was verified.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.75-75
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• 2020

하천법에 의거하여 국내 하천들에는 상당한 국가예산으로 하천정비기본계획이 5-10년 주기로 수립되고 있으며, 홍수위 계산을 위한 HEC-RAS 모의에 필요한 하천단면 등 다양한 하천측량이 실시되고 있다. 그러나, 하천측량자료들은 하천관리지리정보시스템(RIMGIS)에 pdf 보고서 형태로만 제공되고, 원자료는 CAD 형식으로 하천정비계획을 수행한 설계사 등이 분산 소유하고 있어 관리부재로 망실의 우려도 있어, 다른 용도로의 활용성이 상당히 저하되어 있는 실정이다. 그리고, 측량된 CAD 형식의 단면자료 등을 HEC-RAS에 활용할 때, 'Dream'과 같은 툴을 활용하나 거의 수작업에 가까운 시간과 비용이 소요되는 현실에 있다. 본 연구에서는 이러한 문제들을 해결할 수 있는 툴인 RAUT(River information Auto Upload Tool)를 개발하였다., RAUT 툴은 첫째, 실무에서 하천기본계획 수립 시 활용되는 HEC-RAS 1차원 모형의 입력자료를 CAD 측량자료를 직접수기로 입력 및 모의를 실시하는 복잡한 단계를 자동화시키고자 하였다. 둘째, 하천공간정보인 CAD측량 자료를 직접 읽어 표준 데이터 모델 (Arc River)기반 하천공간정보 DB에 자동 업도드하여 전국단위의 하천정비계획의 하천측량자료 관리가 가능하게 할 수 있다. 즉, 만약 RIMGIS가 RAUT와 같은 툴을 사용하면 하천단면과 같은 전국단위 하천측량 자료를 체계적으로 관리할 수 있게 된다는 의미이다. 개발한 RAUT는 제주도 한천유역을 대상으로 하천정비기본계획의 하천공간정보 CAD자료를 읽어들여 mySQL기반 공간 DB로 구축하고, 구축된 DB로부터 HEC-RAS 1차원 모의 실시하기 위한 지형자료를 자동으로 생성시키는 과정을 시범적으로 구현하였다.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.119-124
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• 2023

Deep learning has recently demonstrated conspicuous efficacy across diverse domains than traditional machine learning techniques, as the most popular approach for pattern recognition. The classification problems for tabular data, however, are remain for the area of traditional machine learning. This paper introduces a novel network module designed to tabular data into high-dimensional tensors. The module is integrated into conventional deep learning networks and subsequently applied to the classification of structured data. The proposed method undergoes training and validation on four datasets, culminating in an average accuracy of 90.22%. Notably, this performance surpasses that of the contemporary deep learning model, TabNet, by 2.55%p. The proposed approach acquires significance by virtue of its capacity to harness diverse network architectures, renowned for their superior performance in the domain of computer vision, for the analysis of tabular data.

• Industry Promotion Research
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• v.8 no.4
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• pp.131-137
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• 2023

This study collected and analyzed articles related to military sex crimes covered in the news from February 2019 to May 28, 2022 in order to identify problems arising from sexual crimes in the military. In order to understand the current status of military sexual violence reported in the media, articles were collected using BIGKinds, a news big data analysis system, and using the Textom program, the study was conducted using frequency analysis by period, word cloud, and semantic network analysis techniques for keywords. The study was conducted using the technique. As a result of data analysis, first, it was confirmed that the public's attention was focused on the victims in reports related to sex crimes within the military. Second, the problem of the lukewarm system of the relevant authorities in responding to sex crimes was revealed. Third, there was a lack of support for victims of sex crimes.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.241-250
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• 2023

Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies. Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis. Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword "unmanned cafe" was at the center of the keyword cluster. Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.193-203
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• 2024

In a situation where news usage through YouTube is rapidly increasing, this study investigated which attributes of issues news producers prominently report on based on the two-stage agenda setting theory to empirically investigate the influence of various news producers on YouTube. Through the research results, we confirmed that broadcasters have the influence to set the agenda and form public opinion on YouTube, and discovered the possibility of a two-stage agenda setting effect occurring in the YouTube environment. We criticized whether news producers abuse emotional words due to their partisanship when reporting political issues, and discussed that an emotional approach to political issues can have a negative impact on news users' perception of reality.

• Journal of IKEEE
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• v.28 no.2
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• pp.168-173
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• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.81-88
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• 2024

Despite the rapid growth of the generative AI market and significant interest from domestic companies and institutions, concerns about the provision of inaccurate information and potential information leaks have emerged as major factors hindering the adoption of generative AI. To address these issues, this paper designs and implements a question-answering system based on the Retrieval-Augmented Generation (RAG) architecture. The proposed method constructs a knowledge database using Korean sentence embeddings and retrieves information relevant to queries through optimized searches, which is then provided to the generative language model. Additionally, it allows users to directly manage the knowledge database to efficiently update changing business information, and it is designed to operate in a private network to reduce the risk of corporate confidential information leakage. This study aims to serve as a useful reference for domestic companies seeking to adopt and utilize generative AI.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.

• Journal of Practical Agriculture & Fisheries Research
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• v.23 no.2
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• pp.71-86
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• 2021

This study was conducted to find information on the university's image from words related to 'Korea National College of Agriculture and Fisheries (KNCAF)' in Naver News. For this purpose, word frequency analysis, TF-IDF evaluation and semantic network analysis were performed using web crawling technology. In word frequency analysis, 'agriculture', 'education', 'support', 'farmer', 'youth', 'university', 'business', 'rural', 'CEO' were important words. In the TF-IDF evaluation, the key words were 'farmer', 'dron', 'agricultural and livestock food department', 'Jeonbuk', 'young farmer', 'agriculture', 'Chonju', 'university', 'device', 'spreading'. In the semantic network analysis, the Bigrams showed high correlations in the order of 'youth' - 'farmer', 'digital' - 'agriculture', 'farming' - 'settlement', 'agriculture' - 'rural', 'digital' - 'turnover'. As a result of evaluating the importance of keywords as five central index, 'agriculture' ranked first. And the keywords in the second place of the centrality index were 'farmers' (Cc, Cb), 'education' (Cd, Cp) and 'future' (Ce). The sperman's rank correlation coefficient by centrality index showed the most similar rank between Degree centrality and Pagerank centrality. The KNCAF articles of Naver News were used as important words such as 'agriculture', 'education', 'support', 'farmer', 'youth' in terms of word frequency. However, in the evaluation including document frequency, the words such as 'farmer', 'dron', 'Ministry of Agriculture, Food and Rural Affairs', 'Jeonbuk', and 'young farmers' were found to be key words. The centrality analysis considering the network connectivity between words was suitable for evaluation by Cd and Cp. And the words with strong centrality were 'agriculture', 'education', 'future', 'farmer', 'digital', 'support', 'utilization'.