• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.360-363
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• 2022

According to the 2018 Kaggle ML & DS Survey, among the proportions of frameworks for machine learning and data science, TensorFlow and Keras each account for 41.82%. It was found to be 34.09%, and in the case of development programming, it is confirmed that about 82% use Python. A significant number of machine learning and deep learning structures utilize the Keras framework and Python, but in the case of Python, distribution and execution are limited to the Python script environment due to the script language, so it is judged that it is difficult to operate in various environments. This paper implemented a machine learning and deep learning system using C# and Keras running in Visual Studio 2019. Using the Mnist dataset, 100 tests were performed in Python 3.8,2 and C# .NET 5.0 environments, and the minimum time for Python was 1.86 seconds, the maximum time was 2.38 seconds, and the average time was 1.98 seconds. Time 1.78 seconds, maximum time 2.11 seconds, average time 1.85 seconds, total time 37.02 seconds. As a result of the experiment, the performance of C# improved by about 6% compared to Python, and it is expected that the utilization will be high because executable files can be extracted.

• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.163-164
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• 2015

본 연구는 특수목적영어분인 해사영어코퍼스의 구축을 목적으로 한다. 구축과정에서 코퍼스 구축에 필요한 대표성과 균형성을 고려하여 네 가지 장르인 학술, 뉴스, 법, 책으로 나누고 각 하위코퍼스를 백만 단어씩 구축하였다. 코퍼스 구축과정에서 웹사이트와 PDF형태의 자료에서 텍스트만을 수집하고 정제하기 위하여 파이썬(Python) 프로그래밍 코딩을 하였고 무료 공개 프로그램도 병행하였다. 앞으로 해사영어코퍼스는 해사영어어휘교육에 필요한 단어목록제공이나 예문 검색 등을 통한 자료중심학습법에 활용될 수 있을 것이다. 또한 본 연구의 코퍼스구축 과정은 다른 분야의 ESP코퍼스 구축에도 응용 될 수 있을 것이다.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1663-1672
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• 2021

There are various typing practice applications. In addition, research cases on learning applications that support typing practice have been reported. These services are usually provided in a way that utilizes their own built-in text. Learners collect various contents through web services and use them a lot for learning. Therefore, this paper proposes a learning application to increase the learning effect by collecting vast amounts of web content and applying it to typing practice. The proposed application is implemented using Tkinter, a GUI module of Python. BeautifulSoup module of Python is used to extract information from the web. In order to process the extracted data, the NLTK module, which is an English data preprocessor, and the KoNLPy module, which is a Korean language processing module, are used. The operation of the proposed function is verified in the implementation and experimental results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.73-79
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• 2022

The Python language widely used in artificial intelligence (AI) projects is an interpreter language, and errors occur at runtime. In order to prevent project failure due to errors, it is necessary to handle exceptions in code that can cause exceptional situations in advance. In particular, in AI projects that require a lot of resources, exceptions that occur after long execution lead to a large waste of resources. However, since exception handling depends on the developer's experience, developers have difficulty determining the appropriate exception to catch. To solve this need, we propose an approach that recommends exceptions to catch to developers during development by learning the existing exception handling statements. The proposed method receives the source code of the try block as input and recommends exceptions to be handled in the except block. We evaluate our approach for a large project consisting of two frameworks. According to our evaluation results, the average AUPRC is 0.92 or higher when performing exception recommendation. The study results show that the proposed method can support the developer's exception handling with exception recommendation performance that outperforms the comparative models.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.304-309
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• 2017

Programmers use multiple languages to reuse legacy code best suited to their problems. However, it is quite challenging to develop error-free multilingual programs because new types of bugs occur since misunderstanding about language interfaces such as Java Native Interface (JNI) and Python/C. There is a considerable amount of research to overcome multilingual program bugs and errors but these researches have less consideration about substantiality of programming languages, language interfaces, and bugs to evaluate their analyses and tools. In this paper, we have identified and establish substantiality of multilingual programming research with empirical study about diversity and interoperability of programming languages in Ubuntu software ecosystem based on real-world statistical data.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.117-129
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• 2020

The purpose of this paper is to explore ways to assess computational thinking from a formative perspective and to design a process for assessing programming learning using Python. Therefore, this study explored the computational thinking domain and analyzed research related to assessment design. Also, this study identified the areas of Python programming learning that beginners learn and the areas of computational thinking ability that can be obtained through Python learning. Through this, we designed an assessment method that provides feedback by analyzing syntax corresponding to computational thinking ability. Besides, self-assessment is possible through reflective thinking by using the flow-chart and pseudo-code to express ideas, and peer feedback is designed through code sharing and communication using community.

• Journal of The Korean Association of Information Education
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• v.22 no.2
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• pp.205-212
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• 2018

As a way to improve the software education ability of the pre - service teachers, we conducted programming learning using two types of programming tools (Python and Scratch) at the regular course time. In programming learning, various types of errors, which are factors that continuously hinder interest, achievement and creativity, were collected and analyzed by type. By using the analyzed data, it is possible to improve the ability of pre-service teachers to cope with the errors that can occur in the software education to be taught in the elementary school, and to improve the learning effect. In this study, logic error (37.63%) was the most frequent type that caused the most errors in programming in both conventional language that input text and language that assembles block. In addition, the detailed errors that show a lot of differences in the two languages are the errors of Python (14.3%) and scratch (3.5%) due to insufficient use of grammar and other errors.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.369-376
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• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.74-75
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• 2017

In this paper, we analyzed 3,668 papers of JKIICE from 2007 to 2016 using text mining methods for understanding research fields. We used web scraping programs of Python language for data collection, and utilized topic modeling methods based on LDA algorithm implemented by R language. In the results, we verified that representative research areas of JKIICE could be downsized to 9 areas only by the analysis though the submission areas were 19 areas by 2016.