• 한국융합학회논문지
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• 제12권8호
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• pp.47-53
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• 2021

4차 산업에 대한 관심이 증폭되면서 데이터를 활용한 과학적 방법론이 발전하고 있지만 부동산 분야에 대한 연구는 데이터 수집의 한계점을 내포하고 있다. 더불어 일반 시장 참여자들의 지식이 확장되면서 정성적인 심리가 부동산 시장에 미치는 영향이 커지고 있다. 때문에 본 연구에서는 기존의 원천 데이터가 아닌 심리적 부분을 반영한 정량 데이터를 텍스트마이닝과 k-meas 알고리즘을 통해 수집하는 방안을 제안하고 수집된 데이터를 바탕으로 인공신경망 학습을 통해 주택 지수의 방향성을 예측하고자 한다. 2012년부터 2019년까지의 데이터를 학습 기간으로 하고 2020년도를 예측 기간으로 설정하여 실험을 진행한 결과, 두 가지 CASE에서 예측 능력이 약 80% 이상으로 우수하였고 주택지수의 상승 구간에서의 예측 강도 또한 우수한 결과를 보였다. 본 연구를 통해서 의사결정에 있어서 부동산 시장 참여자들에게 인공신경망과 같은 과학적 방식의 활용도 증가 및 고전적 방식에서 벗어난 원천 데이터의 대체 데이터 확보 등에 대한 노력이 증진되기를 기대한다.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.269-274
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• 2022

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum N_c = 100~10000 and the signal to noise ratio SNR = 0.05-1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the N_c>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of ²³⁵U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3034-3042
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• 2022

The results of radioecological monitoring of the cooling pond Beloyarsk NPP (Russia) have been presented. The influence of waste technological waters of thermal and fast NPP reactors on the content of artificial radionuclides in surface waters and bottom sediments of the Beloyarsk reservoir has been studied. The long-term dynamics of the specific activity of ⁶⁰Co, ⁹⁰Sr, ¹³⁷Cs and ³H in the main components of the freshwater ecosystem at different distances from the source of radionuclide discharge has been estimated. Critical radionuclides (⁶⁰Co and ¹³⁷Cs), routes of their entry and periods of maximum discharge of radioisotopes into the cooling pond have been determined. It is shown that the technology of electricity generation at Beloyarsk NPP, based on fast reactors, has a much smaller effect on the flow of artificial radionuclides into the freshwater ecosystem of the reservoir. During the entire period of monitoring studies, the decrease in the specific activity of radionuclides from NPP origin in surface waters was 4.3-74.5 times, in bottom sediments 10-505 times. The maximum discharge of artificial radionuclides into the reservoir was noted during the period of restoration and decontamination work aimed at eliminating emergencies at the AMB thermal reactors of the first stage of the Beloyarsk NPP.

• 한국지열·수열에너지학회논문집
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• 제18권3호
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• pp.1-6
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• 2022

Ground source heat pump (GSHP) system is highly efficient and environment-friendly and supplies heating, cooling and hot water to buildings. For an optimal design of the GSHP system, the ground thermal properties should be determined to estimate the heat exchange rate between ground and borehole heat exchangers (BHE) and the system performance during long-term operating periods. However, the process increases the initial cost and construction period, which causes the system to be hindered in distribution. On the other hand, much research has been applied to the artificial neural network (ANN) to solve problems based on data efficiently and stably. This research proposes the predictive performance model utilizing ANN considering local characteristics and weather data for the predictive performance model. The ANN model predicts the entering water temperature (EWT) from the GHEs to the heat pump for the modular GHEs, which were developed to reduce the cost and spatial disadvantages of the vertical-type GHEs. As a result, the temperature error between the data and predicted results was 3.52%. The proposed approach was validated to predict the system performance and EWT of the GSHP system.

• 융합신호처리학회논문지
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• 제23권3호
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• pp.173-178
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• 2022

최근 인공지능, 빅데이터, IoT 등의 다양한 컴퓨팅 기술이 발달하고 있다. 특히 콘텐츠 및 의료 산업 등 여러 분야에서 인공지능 기반의 딥페이크(Deepfake) 기술이 다양하게 활용되고 있다. 딥페이크 기술이란 딥러닝과 fake의 합성어로, AI의 핵심기술인 딥러닝을 통해 사람의 얼굴이나 신체를 합성하여 억양, 목소리 등을 따라 하게 만드는 기술이다. 본 논문은 딥페이크 기술을 활용하여 애니메이션 모델과 실제 인물사진의 합성을 통한 가상 캐릭터생성을 연구한다. 이를 통해 애니메이션 제작과정에서 일어나는 여러 가지 비용 손실을 최소화하고 작가들의 작업을 지원할 수 있다. 또한, 딥페이크 오픈소스가 인터넷에 퍼짐에 따라 많은 문제들이 나타나면서 딥페이크 기술을 악용한 범죄가 성행하고 있다. 본 연구를 통해서 딥페이크 기술을 성인물이 아닌 아동물에 적용하여 이 기술에 대한 새로운 관점을 제시한다.

• 정보보호학회논문지
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• 제33권3호
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• pp.411-425
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• 2023

오늘날 AI(Artificial Intelligence) 기술은 다양한 분야에서 활용 목적에 맞게 분류, 회기 작업을 수행하며 광범위하게 활용되고 있으며, 연구 또한 활발하게 진행 중인 분야이다. 특히 보안 분야에서는 예기치 않는 위협을 탐지해야 하며, 모델 훈련과정에 알려진 위협 정보를 추가하지 않아도 위협을 탐지할 수 있는 비 지도학습 기반의 이상 탐지 기법이 유망한 방법이다. 하지만 AI 판단에 대한 해석 가능성을 제공하는 선행 연구 대부분은 지도학습을 대상으로 설계되었기에 학습 방법이 근본적으로 다른 비 지도학습 모델에 적용하기는 어려우며, Vision 중심의 AI 매커니즘 해석연구들은 이미지로 표현되지 않는 보안 분야에 적용하기에 적합하지 않다. 따라서 본 논문에서는 침해공격의 원본인 최적화 Reference를 탐색하고 이와 비교함으로써 탐지된 이상에 대한 해석 가능성을 제공하는 기법을 활용한다. 본 논문에서는 산출된 Reference를 기반으로 실존 데이터에서 가장 가까운 데이터를 탐색하는 로직을 추가 제안함으로써 실존 데이터를 기반으로 이상 징후에 대한 더욱 직관적인 해석을 제공하고 보안 분야에서의 효과적인 이상 탐지모델 활용을 도모하고자 한다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

• 분석과학
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• 제36권1호
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• pp.22-31
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• 2023

As the use of e-liquid cigarettes is rapidly increasing worldwide, it multiplies the potential risk undisclosed to the health of non- and smokers. To reduce the hazard, each country has its own set of regulations for controlling e-liquids. In Korea, the narrow definition of tobacco makes it difficult and have been steadily occurring tax evasion exploiting the difference in natural and artificial nicotine. Therefore, it is very important to distinguish source of nicotine for their regulation. To find biochemical discriminant markers, this study established analysis methods based on high-performance liquid chromatography coupled with diode array detector (HPLC-DAD) and high-performance liquid chromatography coupled with triple Quadrupole mass spectrometry (HPLC-MS/MS) for nicotine enantiomers and tobacco alkaloids targeted using the difference in pathways of nicotine biosynthesis and chemical synthesis. The method was validated by experimenting linearity (R² > 0.999), recovery (80.99-108.41 %), accuracy (94.11-109.73 %) and precision (0.04-8.27 %). Then, the results for discrimination of the nicotine obtained from analysis of 65 commercial e-liquid products available in Korean market was evaluated. The method successfully applied to the e-liquids and one sample labelled 'synthetic nicotine' for tax exemption was found to contain a natural nicotine product. This method can be used to determine whether an e-liquid product uses natural or artificial nicotine and monitor non-taxable e-liquid products. The method is more scientific than the existing one, which relies only on field evidence.

• 대한소아치과학회지
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• 제51권3호
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• pp.299-309
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• 2024

This study aimed to assess the competency of artificial intelligence (AI) in pediatric dentistry and compare it with that of dentists. We used open-source data obtained from the Korea Health Personnel Licensing Examination Institute. A total of 32 item multiple-choice pediatric dentistry exam questions were included. Two AI-based chatbots (ChatGPT 3.5 and Gemini) were evaluated. Each chatbot received the same questions seven times in separate chat sessions initiated on April 25, 2024. The accuracy was assessed by measuring the percentage of correct answers, and consistency was evaluated using Cronbach's alpha coefficient. Both ChatGPT 3.5 and Gemini demonstrated similar accuracy, with no significant differences observed between them. However, neither chatbot achieved the minimum passing score set by the Pediatric Dentistry National Examination. However, both chatbots exhibited acceptable consistency in their responses. Within the limits of this study, both AI-based chatbots did not sufficiently answer the pediatric dentistry exam questions. This finding suggests that pediatric dentists should be aware of the advantages and limitations of this new tool and effectively utilize it to promote patient health.

• 한국균학회지
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• 제36권1호
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• pp.31-35
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• 2008

잎새버섯의 생육환경 중에 광이 자실체 발생 및 생육에 미치는 영향을 구명하기 위하여 광질 3종류(청색광, 녹색광, 백색광), 광량 4수준(200, 500, 8000, 1200 lux)으로 시험한 결과는 다음과 같다. 광질이 자실체 발생 및 생육에 미치는 영향을 조사한 결과 백색광의 발이율이 청색, 적색보다 높고 초발이 소요일수도 3일로 청색광, 녹색광의 5일보다 빠른 것으로 나타났다. 생육일수는 백색광과 청색광이 14일로 녹색광(16일)보다 2일 빠르며, 전체 재배일수 또한 백색광이 52일로 청색광보다 1일, 녹색광보다 4일 단축되었다. 봉지당 수량성에서도 백색광이 242 g 으로 청색광(230 g)과 녹색광(216 g)보다 우수하였다. 백색광의 광량이 지질체 발생 및 생육에 미치는 영향을 조사한 결과 200 lux의 광량에서 발이율이 가장 우수하고, 초발이 소요일수도 4일로 500, 800, 1200 lux의 $7{\sim}8$일보다 $3{\sim}4$일 정도 빠른 것으로 나타났다. 생육일수는 500 lux가 9일로 가장 빨랐으며, 전체 재배일수는 200, 500 lux가 49일로 800, 1200 lux의 51일보다 2일정도 빨라 광량이 증가할수록 재배기간이 다소 길어졌다. 봉지당 수량성은 500 lux가 257 g 으로 가장 우수하였고, 광량이 증가할수록 갓의 크기가 신장되고, 갓 색이 다소 진한 것으로 나타났다.