• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.104-108
• /
• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.11a
• /
• pp.306-309
• /
• 2011

컴퓨터의 발전과 함께 음악 프로그램을 이용한 작곡 작업은 전문가부터 일반인에게 이르기까지 사용자의 폭을 넓혀왔다. 본 연구에서는 이러한 작곡 작업을 보다 효과적으로 하기 위하여 다양한 방안을 모색하였다. 단순한 텍스트 기반의 음악 검색이 아닌 미디(Midi) 분석엔진 기반의 다양한 검색방법을 설계하였으며 이를 기반으로 통합 음악제작/검색 솔루션을 구현하였다. 또한, 휴대성(Portability)을 위하여 MS 사의 WindowsPhone7 플랫폼 기반의 어플리케이션 구현을 통해 PC기반의 프로그램을 모바일 환경에서 작동 및 사용할 수 있도록 구현하였다.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.5
• /
• pp.225-237
• /
• 2022

The term "artificial intelligence" is considered a component of sophisticated technological developments, and several intelligent tools have been developed to assist organizations and entrepreneurs in making business decisions. Artificial intelligence (AI) is defined as the concept of transforming inanimate objects into intelligent beings that can reason in the same way that humans do. Computer systems can imitate a variety of human intelligence activities, including learning, reasoning, problem-solving, speech recognition, and planning. This study's objective is to provide responses to the questions: Which factors should be taken into account while deciding whether or not to use AI applications? What role do these elements have in AI application adoption? However, this study proposes a framework to explore the significance and relation of success factors to AI adoption based on the technology-organization-environment model. Ten critical factors related to AI adoption are identified. The framework is empirically tested with data collected by mail surveying organizations in Vietnam. Structural Equation Modeling is applied to analyze the data. The results indicate that Technical compatibility, Relative advantage, Technical complexity, Technical capability, Managerial capability, Organizational readiness, Government involvement, Market uncertainty, and Vendor partnership are significantly related to AI applications adoption.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.461-464
• /
• 2023

최근 GPT-3 와 LLaMa 같은 생성형 거대 언어모델을 활용한 서비스가 공개되었고, 실제로 많은 사람들이 사용하고 있다. 해당 모델들은 사용자들의 다양한 질문에 대해 유창한 답변을 한다는 이유로 주목받고 있다. 하지만 LLMs 의 답변에는 종종 Inconsistent content 와 non-factual statement 가 존재하며, 이는 사용자들로 하여금 잘못된 정보의 전파 등의 문제를 야기할 수 있다. 이에 논문에서는 동일한 질문에 대한 LLM 의 답변 샘플과 외부 지식을 활용한 Hallucination Detection 방법을 제안한다. 제안한 방법은 동일한 질문에 대한 LLM 의 답변들을 이용해 일관성 점수(Consistency score)를 계산한다. 거기에 외부 지식을 이용한 사실검증을 통해 사실성 점수(Factuality score)를 계산한다. 계산된 일관성 점수와 사실성 점수를 활용하여 문장 수준의 Hallucination Detection 을 가능하게 했다. 실험에는 GPT-3 를 이용하여 WikiBio dataset 에 있는 인물에 대한 passage 를 생성한 데이터셋을 사용하였으며, 우리는 해당 방법을 통해 문장 수준에서의 Hallucination Detection 성능이 baseline 보다 AUC-PR scores 에서 향상됨을 보였다.

• Journal of KIBIM
• /
• v.13 no.3
• /
• pp.12-20
• /
• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.24 no.2
• /
• pp.87-92
• /
• 2024

In this paper, a RAG system was implemented using an existing Large Language Model (LLM) and Langchain library to develop a dental consultation chatbot. For this purpose, we collected contents from the webpage bulletin boards of domestic dental university hospitals and constructed consultation data with the advice and supervision of dental specialists. In order to divide the input consultation data into appropriate sizes, the chunk size and the size of the overlapping text in each chunk were set to 1001 and 100, respectively. As a result of the simulation, the Retrieval Augmented LLM searched for and output the consultation content that was most similar to the user input. It was confirmed that the accessibility of dental consultation and the accuracy of consultation content could be improved through the built chatbot.

• Journal of Practical Engineering Education
• /
• v.16 no.3_spc
• /
• pp.267-274
• /
• 2024

One challenge with teaching practical programming classes is the standardization of development tools on student computers. This is particularly true when a complicated setup process is required before beginning to code, or in remote classes, such as those necessitated by the COVID-19 pandemic, where the instructor cannot provide individual troubleshooting assistance. In such cases, students who encounter problems during the setup process may give up on the class altogether before even beginning to code. Therefore, this paper recommends using GitHub Codespaces as a tool for implementing standardized student development environments from day one. Codespaces provides Docker containers that an instructor can configure in such a way as to enable students to practice installing various coding tools within a controlled space, while also providing a language-specific, fully optimized development environment. In addition, Codespaces may be used more effectively in collaboration with GitHub Classroom, which helps instructors manage both the starter code and coding environment in which students work. In this paper, we compare two semesters of university Node.JS programming classes that utilized different development environments: one localized on student computers, the other containerized in Codespaces online. Then, we discuss how GitHub Codespaces and GitHub Classroom can be used to increase the effectiveness of practical programming classes while also increasing student engagement and programming confidence in class.

• Research in Plant Disease
• /
• v.30 no.1
• /
• pp.99-102
• /
• 2024

Integrated pest management is essential for controlling plant diseases that reduce crop yields. Rapid diagnosis is crucial for effective management in the event of an outbreak to identify the cause and minimize damage. Diagnosis methods range from indirect visual observation, which can be subjective and inaccurate, to machine learning and deep learning predictions that may suffer from biased data. Direct molecular-based methods, while accurate, are complex and time-consuming. However, the development of large multimodal models, like GPT-4, combines image recognition with natural language processing for more accurate diagnostic information. This study introduces GPT-4-based system for diagnosing plant diseases utilizing a detailed knowledge base with 1,420 host plants, 2,462 pathogens, and 37,467 pesticide instances from the official plant disease and pesticide registries of Korea. The AI plant doctor offers interactive advice on diagnosis, control methods, and pesticide use for diseases in Korea and is accessible at https://pdoc.scnu.ac.kr/.

• Journal of Information Science Theory and Practice
• /
• v.12 no.3
• /
• pp.36-48
• /
• 2024

The information retrieval (IR) process often encounters a challenge known as query-document vocabulary mismatch, where user queries do not align with document content, impacting search effectiveness. Automatic query expansion (AQE) techniques aim to mitigate this issue by augmenting user queries with related terms or synonyms. Word embedding, particularly Word2Vec, has gained prominence for AQE due to its ability to represent words as real-number vectors. However, AQE methods typically expand individual query terms, potentially leading to query drift if not carefully selected. To address this, researchers propose utilizing median vectors derived from deep median networks to capture query similarity comprehensively. Integrating median vectors into candidate term generation and combining them with the BM25 probabilistic model and two IR strategies (EQE1 and V2Q) yields promising results, outperforming baseline methods in experimental settings.

• Management & Information Systems Review
• /
• v.33 no.4
• /
• pp.1-19
• /
• 2014

This Study focuses on the university students' job attitude and cost of employment preparation. Nowadays, many university and college students spend a big money improving their employment preparation such as studying on foreign language, getting various kinds of certificates and tooth correction, clothing etc. for employment interview. This study investigated the cost of employment preparation and Job attitude of the 484 students of universities and colleges, the analysis of the collected data was conducted with SPSS 12.0 program by using frequency analysis, factor analysis, reliability assessment, correlation test, t-test, one way ANOVA. The university students paid more costs of employment preparation such as a language training abroad, a private training, and clothing than the college students. Also, Allied social science students paid more costs of the language training abroad, and clothing than allied computer science and allied design students. The female students paid more money than male students for tooth correction. The costs of language training abroad, private training and clothing are affected the students' socioeconomic background of a home. Regarding the job attitude of students, the university students are feeling more positive than the college students of the employment efficacy and cognition of the education environment. As result, the differences in the cost of employment preparation by the university type, faculty major course, their sex, and socioeconomic background of a home. The student's employment-efficacy and cognition of the education environment are also differences between the university and the college students. So, to improve the job attitude, developing their ability for employment preparation, educational programs should be arranged in school and continuous researches are needed.