• 한국진공학회지
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• 제11권3호
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• pp.183-188
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• 2002

지금까지 주로 사용해 오던 TMA(trimethylaluminum, AI$(CH_3)_3)$와 $H_2O$를 사용하여 ALD(Atomic Layer Deposition)법으로 증착시킨 AI$(CH_3)_3)$막내의 $OH^{-}$기는 AI$(CH_3)_3)$의 우수한 물성을 악화시키는 불순물 역할을 하므로, 이를 개선하기 위하여 본 연구에서는 TMA와 오존(ozone, $O_3$)을 이용하여 AI$(CH_3)_3)$막을 증착한 후, 산화제 소스로 $H_2O$와 $O_3$을 각각 사용했을 때 그것들이 AI$(CH_3)_3)$막의 유전적 특성에 끼치는 효과에 관하여 비교 조사하였다. XPS 분석결과 $O_3$를 사용한 AI$(CH_3)_3)$막은 $H_2O$를 사용할 때와는 다르게 $OH^{-}$기가 감소됨을 관찰할 수 있었다. 화학적 안정성(chemical inertness)의 척도가 되는 wet 에칭율 또한 $O_3$를 사용한 AI$(CH_3)_3)$막의 경우가 더욱 우수하게 나타났다. TiN을 상부전극으로 한 MIS (metal-insulator-silicon) capacitor 구조로 제작된 AI$(CH_3)_3)$막의 경우 $H_2O$를 사용한 경우 보다 $O_3$를 사용한 경우에 누설전류밀도가 더 낮았고, 절연특성이 더 우수하였으며, $H_2O$보다 $O_3$를 사용했을 때 C-V 전기적이력(hystersis) 곡선의 편차(deviation)가 감소하는 것으로 보아 전기적 특성이 더 향상되었음을 알 수 있었다.

• 한국수학교육학회지시리즈A:수학교육
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• 제63권3호
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• pp.549-571
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• 2024

디지털·AI 기반 교수·학습이 강조됨에 따라 생성형 AI의 교육적 활용에 대한 논의가 활발해지고 있다. 본 연구는 고등학교 1학년 수학 교과서 5종의 예제와 문제 풀이에 대한 ChatGPT 4, Claude 3 Opus, Gemini Advanced의 수학적 성능을 분석하였다. 총 1,317개 문항에 대해 전체 정답률과 기능별 특징을 살펴본 결과, ChatGPT 4의 전체 정답률이 0.85로 가장 높았고, Claude 3 Opus가 0.67, Gemini Advanced가 0.42 순으로 나타났다. 기능별로는 함수 구하기와 증명하기에서 세 모델 모두 높은 정답률을 보였으나, 설명하기와 그래프 그리기에서는 상대적으로 낮은 정답률을 보였다. 특히 경우의 수 세기에서 ChatGPT 4와 Claude 3 Opus가 1.00의 정답률을 보인 반면, Gemini Advanced는 0.56으로 낮았다. 또한 모든 모델이 벤 다이어그램을 이용한 설명하기와 이미지 생성이 필요한 문제에서 어려움을 겪었다. 연구 결과를 바탕으로 교사들은 각 AI 모델의 강점과 한계를 파악하고 이를 수업에 적절히 활용할 수 있을 것이다. 본 연구는 생성형 AI의 수학적 성능을 분석함으로써, 실제 수학 수업에서의 생성형 AI의 활용 가능성을 제시했다는 점에서 의의가 있다. 또한 인공지능시대의 수학 교육에서 교사의 역할을 재정립하는 데 중요한 시사점을 제공하였다. 향후 생성형 AI와 교사의 협력적 교육 모델 개발, AI를 활용한 개별화 학습 방안 연구 등이 필요할 것이다.

• 대한본초학회지
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• 제33권3호
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• pp.55-61
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• 2018

Objectives : Artemisia capillaris Thunberg (AC) and Artemisia iwayomogi Kitamura (AI) have been used without distinguishment since ancient times due to similar appearance. In this study, we compared the inhibitory effects of AC and AI on the expression of inflammatory cytokines induced by lipopolysaccharide (LPS) in murine macrophages. Methods : AC and AI were extracted by reflux with distilled water (DW) and 70% ethanol (EtOH). We investigated the inhibitory effects of AC and AI on the expression of nitric oxide (NO), inducible NO synthase (iNOS) and tumor necrosis $factor-{\alpha}$($TNF-{\alpha}$) induced by LPS in macrophages. Results : Firstly, yield of the samples was higher in order of Artemisia iwayomogi DW Extract (AID), Artemisia iwayomogi 70% EtOH Extract (AIE), Artemisia capillaris DW Extract (ACD) and Artemisia capillaris 70% EtOH Extract (ACE). All of the samples were not toxic in macrophages. The inhibitory effect of the samples on LPS-induced NO expression was stronger in the order of AIE, ACE, AID and ACD. The inhibitory effect of the samples on LPS-induced inducible iNOS expression was stronger in the order of AIE, ACE and AID. Effect of ACD was same with that of AID. In addition, inhibitory effect of the samples on LPS induced $TNF-{\alpha}$expression wes stronger in the order of AIE, ACE, AID and ACD. Conclusion: These results showed that AI would be more effective than AC and 70% EtOH would be more effective than DW as an extraction solvent in inflammatory diseases.

• 스마트미디어저널
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• 제9권3호
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• ETRI Journal
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• 제44권5호
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• pp.859-874
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• 2022

This paper presents a low-power and lightweight human body communication (HBC) receiver with an embedded dummy electrode for improved signal acquisition. The clock data recovery (CDR) circuit in the receiver operates with a low supply voltage and utilizes a clock phase inversion scheme. The receiver is equipped with a main electrode and dummy electrode that strengthen the capacitive-coupled signal at the receiver frontend. The receiver CDR circuit exploits a clock inversion scheme to allow 0.9-V operation while achieving a shorter lock time than at 3.3-V operation. In experiments, a receiver chip fabricated using 130-nm complementary metal-oxide-semiconductor technology was demonstrated to successfully receive the transmitted signal when the transmitter and receiver are placed separately on each hand of the user while consuming only 4.98 mW at a 0.9-V supply voltage.

• Journal of information and communication convergence engineering
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• 제20권3호
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• pp.166-173
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• 2022

Recently, studies have been conducted on intelligent transportation systems (ITS) that provide safety and convenience to humans. Systems that compose the ITS adopt architectures that applied the cloud computing which consists of a high-performance general-purpose processor or graphics processing unit. However, an architecture that only used the cloud computing requires a high network bandwidth and consumes much power. Therefore, applying edge computing to ITS is essential for solving these problems. In this paper, we propose an edge artificial intelligence (AI) device based ITS. Edge AI which is applicable to various systems in ITS has been applied to license plate recognition. We implemented edge AI on a field-programmable gate array (FPGA). The accuracy of the edge AI for license plate recognition was 0.94. Finally, we synthesized the edge AI logic with Magnachip/Hynix 180nm CMOS technology and the power consumption measured using the Synopsys's design compiler tool was 482.583mW.

• 품질경영학회지
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• 제51권3호
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• pp.363-379
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• 2023

Purpose: The main objective of this research is to construct an AI-based Composite Supplementary Index (ACSI) model to achieve accurate predictions of the Composite Index of Business Indicators. By incorporating various economic indicators as independent variables, the ACSI model enables the prediction and analysis of both the leading index (CLI) and coincident index (CCI). Methods: This study proposes an AI-based Composite Supplementary Index (ACSI) model that leverages diverse economic indicators as independent variables to forecast leading and coincident economic indicators. To evaluate the model's performance, advanced machine learning techniques including MLP, RNN, LSTM, and GRU were employed. Furthermore, the study explores the potential of employing deep learning models to train the weights associated with the independent variables that constitute the composite supplementary index. Results: The experimental results demonstrate the superior accuracy of the proposed composite supple- mentary index model in predicting leading and coincident economic indicators. Consequently, this model proves to be highly effective in forecasting economic cycles. Conclusion: In conclusion, the developed AI-based Composite Supplementary Index (ACSI) model successfully predicts the Composite Index of Business Indicators. Apart from its utility in management, economics, and investment domains, this model serves as a valuable indicator supporting policy-making and decision-making processes related to the economy.

• 전자통신동향분석
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• 제39권2호
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• pp.33-42
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• 2024

With the recent rapid development of artificial intelligence (AI) technology, its use is gradually expanding to include creative areas and building new content using generative AI solutions, reaching beyond existing data analysis and reasoning applications. Content creation using generative AI faces challenges owing to technical limitations and other aspects such as copyright compliance. Nevertheless, generative AI may increase the productivity of experts and overcome barriers to creative work by allowing users to easily express their ideas as digital content. Thus, various types of applications will continue to emerge. As images and videos can be created using text input on a prompt, generative AI allows to create and edit digital assets quickly. We present trends in generative AI technology for images, videos, three-dimensional (3D) assets and scenes, digital humans, interactive content, and interfaces. In addition, the prospects for future technological development in this field are discussed.

• 한국수학교육학회지시리즈A:수학교육
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• 제63권3호
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• pp.573-591
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• 2024

본 연구는 AI 기반 수학 교수·학습에 대한 문헌을 체계적, 종합적으로 고찰하여 연구 동향을 탐색하고자 수행되었다. 이를 위해 최근 10년 간의 수학교육 문헌 중 문헌선정기준에 부합하는 57개의 문헌을 연구 대상, 연구 방법, 연구 목적, 학습 내용, AI의 유형, AI의 역할, 교사의 역할 측면에서 체계적 문헌 고찰하였다. 연구 결과, 연구 대상 중 학생을 대상으로 한 연구가 51%로 가장 많은 비중을 차지했으며, 연구 방법 중 양적 연구의 비중이 49%로 가장 높았다. 연구 목적은 효과 분석 44%, 이론적 논의 35%, 수업 사례 탐색 21%로 분포했다. 학습 내용으로 '수와 연산'과 '문자와 식'이 가장 많이 다루어졌고, AI 유형 중 지능형 튜터링 시스템(ITS)이 가장 많이 사용되었다. AI의 역할은 학습자 교수의 비중이 40.4%로 가장 높았으며, 교수자 지원 22.8%, 학습자 지원 21%, 시스템 지원 15.8% 순으로 분포하였다. 교사의 역할은 초기 연구일수록 'AI 수용자'로서의 역할이, 최근 연구일수록 'AI와의 건설적 파트너'로서의 역할이 부각되었고, 각 역할이 교육학적, AI-기술적, 내용적 측면에서 탐색되었다. 이를 통해 국내 수학교육 후속 연구의 방향이 제안되었고, AI 기반 수학 교수·학습에서의 교사가 갖추어야 할 소양이 논의되었다.

• 한국전기전자재료학회논문지
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• 제15권3호
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• pp.238-243
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• 2002

The dielectric properties and synthesis of $LaAIO_3$ ceramics from mixtures of $La_2O_3$ and $AI(OH)_3$ via ground(planetary ball mill) and unground(wet ball mill) were investigated. The single phase $LaAIO_3$ of ground powder was formed at $1000^{\circ}C$, while that of unground powder was formed at $1300^{\circ}C$. Density and grains of ground sample showed 98% of theory density and a uniform size of 0.75\mu\textrm{m}$, respectively, However those of unground sample showed 93% and non-uniform sizes of 4-5 $\mu\textrm{m}$. Dielectric constant and temperature coefficient of capacitance ($\tau$c) of both ground and unground samples were 21~22 and +70~74 ppm$/^{\circ}C$, respectively. Dielectric loss of ground sample(0.0004) was 10 times as low as that of unground sample(0.003) due to a uniform and small gram size.