• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.154-160
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• 2018

This thesis aims to develop PBL (Problem-Based-Learning) problems. Its goal is for some groups of students to creative their own problems and to confirm the effectiveness of PBL as they apply it to AI (Artificial Intelligence) in engineering schools. Modern industrial society needs competent people who have abilities in cooperative learning, self-controlled learning, united knowledge application, and creative problem-solving. Universities need to offer their students the opportunity to improve their problem-solving and cooperative learning abilities in order to train the competent people that society demands. PBL activity is an appropriate learning method for the accomplishment of these goals. The study subjects are 37 sophomore students in H University who are studying 'AI'. Five PBL problems were submitted to the class over a period of 15 weeks. The students wrote and submitted a reflective journal after they finished each PBL activity. In addition, they filled out a class evaluation form to assess the performances of each member when the $5^{th}$ PBL problem activity was accomplished. The study shows that the students experienced the effectiveness of PBL in many fields, such as the comprehension of the studied contents (86.48%), comprehension of cooperative learning (94.59%), authentic experience (75.67%), problem-solving skills (89.18%), presentation skills (97.29%), creativity improvement (81.08%), knowledge acquisition ability (86.48%), communication ability (97.29%), united knowledge application (78.37%), self-directed study ability (86.48%) and confidence (97.29%). Through these methods, the students were able to realize that PBL learning activities play an important role in their learning. These methods prepare and enhance their ability to think creatively, work systematically and speak confidently as they learn to become competitive engineers equipped with the knowledge and skills that modern industrial society demands.

• Electronics and Telecommunications Trends
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• v.32 no.1
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• pp.54-60
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• 2017

사물인터넷은 모든 사람과 사물이 인터넷을 통해 서로 소통하고 새로운 가치를 창출할 수 있는 기술이며, 정보의 확산, 연계, 활용을 가능하게 하는 중요한 연결고리이다. 인지 IoT는 이러한 사물인터넷 인프라와 함께 인공지능 기술을 활용하여, 사물이 스스로 생각하고 판단하며, 보다 잘 연결하고 더 똑똑해지도록 하는 사물지능 실현 기술이다. 본고는 인간 두뇌의 기능을 모방하여 인식, 행동, 인지 능력을 재현해내는 대표 인지 컴퓨팅 기술인 IBM 왓슨, 딥 러닝, 뉴로모픽칩 기술을 요약하며, 또한, 사물수준 지능 실현 사례인 IBM 쿼크, CISCO DMo와 D3의 개발 현황을 소개한다.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.131-131
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• 2022

기후변화의 영향으로 극치강우의 변동성이 커지고 있으며 계획빈도를 초과하는 폭우로 피해가 증가하고 있다. 기존의 물리기반의 홍수예측모델은 개념적 및 구조적 제약과 함께 다양한 유역조건 및 수문기상 조건에 기인한 강우-유출 관계의 불확실성을 고려하는 데 한계가 있다. 특히 한정된 홍수 사상을 통해 구축된 관측 자료로 인해 새로운 홍수 사상 예측 능력이 저조할 수밖에 없다. 따라서 기존 물리모형 기반의 홍수예측과 함께, 딥러닝(deep learning) 모형을 고려한 홍수예측 모델 개발과 개선이 필요하다. 본 연구에서는 다양한 분야에서 활용되는 인공지능(artificial intelligence, AI) 기술을 종합적으로 검토하고, 홍수 예측 측면에서의 활용 가능성 및 신뢰성을 고려하여 AI 기법을 채택하였다. 한강수계에 존재하는 댐 중 일부를 선정하여 대상 댐의 수문·기상학적 자료를 전처리한 후, 인공지능 기반의 홍수예측모형을 구축 및 최적화하였다. 다양한 예측인자와 모델 구성으로 홍수예측력에 대한 평가를 다각적으로 수행함으로써 홍수예측모델의 신뢰성을 제고하였다. 전반적으로 우수한 결과를 도출하였고, 유역면적이 작을수록 결과가 좋았다. 이는 넓은 유역일수록 복잡한 강우-유출 과정이 내재되어 있기 때문으로 판단되며, 넓은 유역에는 본 연구에서 활용한 자료에 추가적인 자료를 도입하여 모형 개선이 이루어져야 할 것으로 판단하였다. 수문 예측 연구에 통계모형이나 기계학습모형의 적용은 많이 있었지만, 딥러닝 기법 활용은 새로운 시도라는 점에서 의미가 있다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.227-234
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• 2023

In this paper, we developed a design thinking-based artificial intelligence education program for middle school students and applied it to verify the impact on creative problem-solving skills. The inspection tool used the Creative Problem Solving Profile Inventory (CPSPI), an inspection tool for measuring creative thinking type ability based on the CPS theory of Hwasun Lee, Jungmin Pyo, Insoo Choe(2014). CPSPI included the steps of evaluating cognitive preferences and cognitive abilities by supplementing the limitations of existing tests, and sharing and persuading one's ideas with others. Before and after applying the design thinking-based artificial intelligence education program, as a result of analyzing the creative problem-solving ability, it increased significantly in all areas. As a result of analyzing the creative problem-solving ability of middle school students, significant results were found in the areas of Problem Detection and Analysis, Idea Generation, Action plan, Execution, Persuasion and Communication. The effect of design thinking was confirmed as a teaching and learning method to improve creative problem-solving ability in artificial intelligence education.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.221-229
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• 2018

Machine learning and artificial intelligence are core technologies for the 4th industrial revolution. However, it is difficult for the general public to get familiar with those technologies because most people lack programming ability. Thus, we developed a Graphic User Interface(GUI) to overcome this obstacle. We adopted TensorFlow and used .Net of Microsoft for the develop. With this new GUI, users can manage data, apply algorithms, and run machine learning without coding ability. We hope that this development will be used as a basis for developing artificial intelligence in various fields.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.41-52
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• 2009

This thesis proposed the poker game achievement engine that makes it possible to execute a game against a gamer and embodied an engine clone that can perform a game on behalf of a gamer by cloning a gamer's game capability, disposition and habits by using the proposed engine. It could be a factor in making a game more intriguing if there would be a game achievement engine that is equipped with the game capability and disposition similar to a gamer. In addition, if a gamer's achievement robot were to be created by a gamer's game achievement capability, disposition for a game and habits, etc. through such a game achievement engine, it could be possible to use it for several sorts of game in a variety of forms.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.1-11
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• 2020

The purpose of this study is to propose the direction of AI education in K-12 education through investigating and analyzing aspects of the purpose, content, and methods of AI education as the curriculum and teacher training factors. We collected and analyzed 9 papers as the primary literature and 11 domestic and foreign policy reports as the secondary literature. The collected literatures were analyzed by applying a descriptive reviews, and the implications were derived by analyzing the curriculum components and TPACK elements for multi-dimensional analysis. As a result of this study, AI education targets were divided into three steps: AI users, utilizer, and developers. In K-12 education, the user and utilizer stages are appropriate, and artificial intelligence literacy must be included for user education. Based on the current computing thinking ability and coding ability for utilizer education, the implication was derived that it is necessary to target the ability to create creative output by applying the functions of artificial intelligence. In addition to the pedagogical knowledge and the ability to use the platform, The teacher training is necessary because teachers need content knowledge such as problem-solving, reasoning, learning, perception, and some applied mathematics, cognitive / psychological / ethical of AI.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.