This study endeavors to enrich investment prospects in cryptocurrency by establishing a rationale for investment decisions. The primary objective involves evaluating the predictability of four prominent cryptocurrencies – Bitcoin, Ethereum, Litecoin, and EOS – and scrutinizing the efficacy of trading strategies developed based on the prediction model. To identify the most effective prediction model for each cryptocurrency annually, we employed three methodologies – AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Prophet – representing traditional statistics and artificial intelligence. These methods were applied across diverse periods and time intervals. The result suggested that Prophet trained on the previous 28 days' price history at 15-minute intervals generally yielded the highest performance. The results were validated through a random selection of 100 days (20 target dates per year) spanning from January 1st, 2018, to December 31st, 2022. The trading strategies were formulated based on the optimal-performing prediction model, grounded in the simple principle of assigning greater weight to more predictable assets. When the forecasting model indicates an upward trend, it is recommended to acquire the cryptocurrency with the investment amount determined by its performance. Experimental results consistently demonstrated that the proposed trading strategy yields higher returns compared to an equal portfolio employing a buy-and-hold strategy. The cryptocurrency trading model introduced in this paper carries two significant implications. Firstly, it facilitates the evolution of cryptocurrencies from speculative assets to investment instruments. Secondly, it plays a crucial role in advancing deep learning- based investment strategies by providing sound evidence for portfolio allocation. This addresses the black box issue, a notable weakness in deep learning, offering increased transparency to the model.

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.

시판 유통 중인 고춧가루의 10종에 대해 미생물 (총 호 기성균수, 대장균군, 효모 & 곰팡이), 이화학적 품질 (수분 함량, pH, 기계적 색도 및 ASTA 색도, 입자 크기)을 확인 하였다. 시판 유통 중인 미생물 농도 측정결과, 총 호기성균, 효모 및 곰팡이는 103-106CFU/g 으로 나타났고, 대장 균군은 2종의 검체에서 103 CFU/g의 분포를 나타내었다. 수분함량은 7.25-12.73%로 나타나, 10종 모두 식품공전의 기준규격에 적합하였다. pH는 4.97~5.15 범위를 나타내어 시판 고춧가루의 건조방법은 각각 다른 것으로 판단되었다. 색도 측정결과 기계적 색도의 E 값은 최저 47.19, 최 고 58.04를 나타내었고, ASTA 색도는 최저 89.31, 최고 98.61로 나타나 제품별로 색도의 뚜렷한 차이가 나타났으나, 기계적 색도와 ASTA 색도의 상관성은 높지 않은 것으로 판단되었다. 고춧가루 10개 검체의 평균 입자크기는 605-1251 um로 나타났고, 분포도는 2종의 시료(RP-2, RP- 3)는 매우 균일한 분포도를 나타낸 반면, RP-9, RP-10은 가장 고르지 못한 분포도를 나타내었다.

The identification characteristics of irradiated (0.5, 1, 2, and 4 kGy) brown rice, soybean, and sesame seeds were investigated using photostimulated luminescence (PSL), thermoluminescence (TL), and hydrocarbon analysis during 12-month storage. PSL-based screening was possible for the irradiated soybean and sesame seed samples up to 6 and 12 months, respectively. The TL glow curve shape, intensity, and ratio enabled the clear dose-dependent discrimination of all the non-irradiated and irradiated samples. The TL intensity decreased during storage, but the TL glow curve did not change qualitatively, which provided enough information to confirm the irradiation treatment of the samples over the storage period. Radiation-induced hydrocarbons were found in all the irradiated samples even at 0.5 kGy, throughout the storage period. 8-Heptadecene (C17:1) and 1,7-hexadecadiene (C16:2) originated from oleic acid, and 6,9-heptadecadiene (C17:2) and 1,7,10-hexadecatriene (C16:3) originated from linoleic acid, can be used as radiation-induced markers in identifying irradiated brown rice, soybean, and sesame seeds.

중첩에너지(FGFE)가 방사선조사식품의 판별 특성에 미치는 영향을 연구하고자, 밀과 대두를 시료로 0-5 kGy의 감마선을 조사한 다음 FGFE 처리된 시료의 발광특성(광자극발광, 열발광)과 발아율의 변화를 처리직후와 6개월 저장 후 비교하였다. 조사식품의 스크리닝 방법으로서 두 시료에 대한 광자극발광(PSL) 분석 결과, 비 조사 시료(0 kGy)는 모두 700 photon counts/min 이하의 음성(negative)을 나타내었고, 1 k

시판 혼합조미료(SS-1, SS-2)를 시료로 하여 조사선원(감마선, 전자선) 및 조사선량(0-20 kGy)에 따른 ESR spectrum의 특성을 비교하고, 방사선조사 유래의 signal에 대한 parameter를 분석하여 조사여부 판별을 뒷받침하는 자료를 확인하였다. 그 결과, 방사선 조사된 조미료 시료에서는 조사선원에 상관없이 특이한 free radical의 ESR signal을 보여주었다. 이 signal은 g-value가 2.031, 2

ESR spectroscopy에 의한 방사선 조사 건조향신료의 조사여부의 정확한 판별에 필요한 기초자료를 얻고자 방사선 조사 유래의 cellulose radical에 대한 parameter를 분석하였다. 건조향신료 분말 4종(고추, 마늘, 양파, 후추)에 대하여 0, 1, 5, 10 kGy의 감마선을 조사한 후 ESR signal을 분석하였다. 방사선 조사된 4종의 건조향신료는 모두 방사선 조사 유래의 triplet signal인 cellulo