• 철학연구
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• 제146권
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• pp.271-307
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• 2018

본 연구는 트랜스휴머니즘 도덕교육의 방향을 찾아보고자 한다. 신경과학이나 공학과 같은 과학기술의 성과를 최대한 활용하여 인간생명의 트랜스휴먼적 향상을 도모하는 방식에는 유전자 조작 방안, 도덕적 인공지능을 활용하는 사이보그 방안, 약리학적 방안이 있겠다. 하지만 본 연구에서는 유전자 조작 방안과 도덕적 인공지능을 활용하는 사이보그 방안을 중심으로 살펴보았다. 그리고 도덕적 인공지능을 활용한 사이보그 방안을 지속적으로 추구될 수 있는 실현성 있는 방안으로 보았다. 반면에 트랜스휴머니스트들이 기존의 전통적 도덕교육이 너무 느리고 비효과적이라고 보지만 유전자 조작 보다는 후성 유전학에 근거한 최상의 양육과 교육이 지금의 기술적인 제약 하에서는 오히려 효과적인 접근임을 알 수 있었다. 또한 연구자는 트랜스휴먼의 방향에 관한 논의에서 장기적으로 유전자 조작방안보다는 도덕적 인공지능이나 사이보그 방안이 윤리학적 문제점과 기술적 어려움을 보다 잘 극복할 수 있다고 판단한다. 따라서 논문에서 이 방안이 도덕교육의 모델로 적합할 수 있음을 제시하였다. 연구자는 트랜스휴머니즘의 인간향상에 관한 도덕성 논쟁에서 주로 공리주의적 차원에서 우리가 얻게 될 잠재적 이득을 중심으로 논의하고자 하였다. 특히 연구자는 트랜스휴머니즘과 관련하여 새로이 개발되는 기술의 잠재적 위험성을 줄이면서 그 기술이 목표로 하는 정당성과 효율성을 택하는 방식으로 진행되어야 한다고 강조하였다. 물론 사회적으로는 도덕적 인간향상의 윤리적 사용과 관련된 윤리학적 숙고와 법령 마련, 교육적으로는 인간의 도덕적 향상의 윤리 문제에 보다 추가적인 논의가 필요하겠지만 도덕적 인간향상의 방식을 근본적으로 부정하는 것은 대안이 아님을 제시하였다. 결국 트랜스휴머니즘의 시대에 다양한 도덕교육의 방식은 열려져 있으며, 이런 방안들을 꼼꼼하게 검토하고 지혜롭게 이를 적용해 나가야 하는 과제가 우리 앞에 놓여 있다고 할 수 있다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2018년도 춘계학술대회
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• pp.314-318
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• 2018

이 연구는 고령화 시대에 실버 인구가 급증하는 4차산업 시대에 따라 새롭게 생기는 지식과 정보활용 능력을 ICT 교육 효과로 인해 실버세대들의 IOT 생활 적응에 미치는 영향에 따른 초점을 두고 분석하여 바림직한 교육 방향을 모색 하는데 목적을 둔다. 문헌연구 및 설문조사로 실제 실버 정보화교육 대상자로 분석한 결과 대체로 정보화 교육은 실버 세대들의 전 삶의 질 향상 및 실제 IOT 기반의 웹 모바일 사용에도 긍정적 효과를 미치고 있음을 확인할 수 있었다. 그러나 정보화 교육의 기간에 따라 역량에 차이를 두고 있었으며, 전 직업 및 학력 등 다양한 기준에 따른 세분화와 이에 따른 4차 산업시대에 맞춤형 교육의 제공 및 실 삶의 질, 후 취업의 ICT 교육의 바람직한 방향에도 개괄적인 논의 및 과정 제시를 병행 한다.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1151-1163
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• 2023

Objective: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by comparing it with a Greulich-Pyle-based deep-learning model. Materials and Methods: A convolutional neural network was trained to predict age according to the bone development shown on a hand radiograph (bone age) using 21036 hand radiographs of Korean children and adolescents without known bone development-affecting diseases/conditions obtained between 1998 and 2019 (median age [interquartile range {IQR}], 9 [7-12] years; male:female, 11794:9242) and their chronological ages as labels (Korean model). We constructed 2 separate external datasets consisting of Korean children and adolescents with healthy bone development (Institution 1: n = 343; median age [IQR], 10 [4-15] years; male: female, 183:160; Institution 2: n = 321; median age [IQR], 9 [5-14] years; male: female, 164:157) to test the model performance. The mean absolute error (MAE), root mean square error (RMSE), and proportions of bone age predictions within 6, 12, 18, and 24 months of the reference age (chronological age) were compared between the Korean model and a commercial model (VUNO Med-BoneAge version 1.1; VUNO) trained with Greulich-Pyle-based age as the label (GP-based model). Results: Compared with the GP-based model, the Korean model showed a lower RMSE (11.2 vs. 13.8 months; P = 0.004) and MAE (8.2 vs. 10.5 months; P = 0.002), a higher proportion of bone age predictions within 18 months of chronological age (88.3% vs. 82.2%; P = 0.031) for Institution 1, and a lower MAE (9.5 vs. 11.0 months; P = 0.022) and higher proportion of bone age predictions within 6 months (44.5% vs. 36.4%; P = 0.044) for Institution 2. Conclusion: The Korean model trained using the chronological ages of Korean children and adolescents without known bone development-affecting diseases/conditions as labels performed better in bone age assessment than the GP-based model in the Korean pediatric population. Further validation is required to confirm its accuracy.

• Computers and Concrete
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• 제27권4호
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• 지능정보연구
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• 제23권1호
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• pp.95-108
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• 2017

최근 알파고의 등장으로 딥러닝 기술에 대한 관심이 고조되고 있다. 딥러닝은 향후 미래의 핵심 기술이 되어 일상생활의 많은 부분을 개선할 것이라는 기대를 받고 있지만, 주요한 성과들이 이미지 인식과 자연어처리 등에 국한되어 있고 전통적인 비즈니스 애널리틱스 문제에의 활용은 미비한 실정이다. 실제로 딥러닝 기술은 Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Deep Boltzmann Machine (DBM) 등 알고리즘들의 선택, Dropout 기법의 활용여부, 활성 함수의 선정 등 다양한 네트워크 설계 이슈들을 가지고 있다. 따라서 비즈니스 문제에서의 딥러닝 알고리즘 활용은 아직 탐구가 필요한 영역으로 남아있으며, 특히 딥러닝을 현실에 적용했을 때 발생할 수 있는 여러 가지 문제들은 미지수이다. 이에 따라 본 연구에서는 다이렉트 마케팅 응답모델, 고객이탈분석, 대출 위험 분석 등의 주요한 분류 문제인 이진분류에 딥러닝을 적용할 수 있을 것인지 그 가능성을 실험을 통해 확인하였다. 실험에는 어느 포르투갈 은행의 텔레마케팅 응답여부에 대한 데이터 집합을 사용하였으며, 전통적인 인공신경망인 Multi-Layer Perceptron, 딥러닝 알고리즘인 CNN과 RNN을 변형한 Long Short-Term Memory, 딥러닝 모형에 많이 활용되는 Dropout 기법 등을 이진 분류 문제에 활용했을 때의 성능을 비교하였다. 실험을 수행한 결과 CNN 알고리즘은 비즈니스 데이터의 이진분류 문제에서도 MLP 모형에 비해 향상된 성능을 보였다. 또한 MLP와 CNN 모두 Dropout을 적용한 모형이 적용하지 않은 모형보다 더 좋은 분류 성능을 보여줌에 따라, Dropout을 적용한 CNN 알고리즘이 이진분류 문제에도 활용될 수 있는 가능성을 확인하였다.

• 한국기계가공학회지
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• 제19권11호
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• 한국멀티미디어학회논문지
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• 제23권10호
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• pp.1296-1306
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• 2020

Childhood verbal communication impacts children's language skills and has a positive effect as partners use more vocabulary. But reduction in family time, caused by lowered age for private education and so on, has reduced the chance for children to speak with partners who have a proficient language skill. This vacancy was naturally occupied by the media, which has become one of the cornerstones of the growth of kids' contents. Kids contents are making various attempts to expand the breadth of services. But most contents still focus on unilateral visual information delivery yet, so there is a limit to satisfy the vacancy of conversation partners. Therefore this paper suggests an ECA(Embodied conversational agent) to induce children's spoken conversation using a virtual character frequently used in kids contents. This system is implemented by the voice bot and agent model produced using an IBM assistant and Unity. As a result of using ECA for 66 children of 5-9 years old, it showed meaningful results in terms of induction of verbal communication.

• 한국의류학회지
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• 제47권6호
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• pp.1137-1156
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• 2023

With the advent of artificial intelligence (AI) during the Fourth Industrial Revolution, the fashion industry has simplified the production process and overcome the technical difficulties of design. This study anticipates likely changes in the digital age and develops a model that will allow consumers to design textile patterns using AI technology. Previous studies and industrial examples of AI technology's use in the textile design industry were investigated, and a textile pattern was developed using an AI algorithm. A new textile design model was then proposed based on its application to both virtual and physical clothing. Inspired by traditional Korean masks and props, AI technology was used to input color data from open application programming interface images. By inserting these into various repeating structures, a textile design was developed and simulated as garments for both virtual and real garments. We expect that this study will establish a new textile design development method for Generation Z, who favor customized designs. This study can inform the use of personalization in generative textile design as well as the systemization of technology-driven methods for customized and participatory textile design.

• 대한물리치료과학회지
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• 제28권1호
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• pp.62-75
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• 2021

Background: The world has entered the age of biotechnology and artificial intelligence, and encouraging students to test the value of information and knowledge ie to become information fluent, is becoming more important. The education system is also changing in order to adapt to the times. As a part of this, the cultivation of creative talent is a core goal of many nation states, and Israel's Jewish education methods are attracting attention; havruta (or chavrusa) is one such method. This study aims to effects of havruta class on learning attitudes and class satisfaction in a class of college physical therapy students. Design: Pretest-posttest design. Methods: The subjects were 95 students in College A. The learning attitudes questionnaire were used by the Korea Educational Development Institute, and the class satisfaction questionnaire before and after intervention. Results: The results showed significant differences in learning habits about physical therapy of learning attitudes (p<.05) and class methods and contents attention and understanding (p<.05), class interest of class satisfaction (p<.05). Conclusion: These results suggest that havruta class improves learning attitudes and class satisfaction. Therefore, follow-up study is needed to apply the havruta class in various students and teaching methods.

• 인터넷정보학회논문지
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• 제24권4호
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.