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.

Cryptocurrency transactions are subject to private law regulations. This article reviews the legal nature of cryptocurrency from the perspective of general civil law theory. The undeniable nature of property in cryptocurrency has led to an increased demand for judicial systems to effectively operate in regards to cryptocurrency. The right to cryptocurrency cannot be regarded as intellectual property rights. Cryptocurrency cannot also be regarded as a bond, as it is merely units of information listed in the distributed ledger with no counterparty. Cryptocurrency can be understood as an “things” under civil law because there is a possibility of management through an electronic wallet private key and also independence through a distributed ledger. Among the requirements of an things, the requirement of ‘corporeal things’ or ‘natural force’ can be flexibly interpreted in regards of transactions between related parties. On the other hand, cryptocurrency cannot be regarded as “money” among things. Cryptocurrency cannot function as a “measure of value”, which is a fundamental function of money, due to its inherent nature of volatility and price differences between its exchanges. As a result, the legal nature of cryptocurrency can be recognized as a things, not money, through which current existing judicial systems such as compulsory execution law and bankruptcy law becomes able to operate in regards to cryptocurrency, ultimately promoting legal predictability. However, legislation on cryptocurrency should ultimately be completed through legislation, not interpretation. This requires further in-depth discussion in academic and practice sectors.

일반적으로 사람들은 자기고양 동기로 인해 나와 관련된 것을 더 좋게 평가하는 것으로 알려져 있으며, 대표적인 예시로 소유효과가 있다. 하지만 우리는 경험을 통해 동일한 물건이어도 내 것보다 타인의 것이 더 좋아 보일 때가 있다는 것을 알고 있다. 최근 연구들은 이러한 현상을 소유효과의 역전 현상이라 설명한다. 본 연구는 나-타인 소유 물에 대한 가치 추정이 개인의 조절초점 성향을 통해 달라지는지 확인함으로써 소유효과와 소유효과의 역전 현상을 조절하는 심리학적 변인을 탐색해 보고자 하였다. 이에 따라 지각된 이득과 손실에 대한 관점이 다르다고 알려진 개인의 조절초점 성향이 나-타인 소유주에 따른 소유효과를 조절할 것으로 예상하였으며, 외국인 참가자를 대상으로 익숙하지 않은 타 국가의 화폐를 보여주고 돈의 가치를 모국의 화폐 가치로 추정하도록 하였다. 그 결과, 예방초점 성향이 높은 사람들보다 향상초점 성향이 높은 사람들이 더 돈의 가치를 높게 추정하는 것으로 나타났다. 하지만 내가 아닌 다른 사람의 돈이라고 생각했을 때는 조절초점 성향의 조절효과가 유의하지 않았다. 이는 조절초점이라는 개인차 수준에 따라 소유효과가 더 강하게 나타날 수도 있고, 사라질 수도 있다는 것을 시사한다. 연구 결과를 바탕 으로 연구의 의의, 한계점 및 후속 연구에 대해 논의하였다.

본 논문은 부상하고 있는 지역화폐를 중심으로 블록체인 기반 게이미피케이션의 대한 융합 연구이다. 먼저, 블 록체인과 지역화폐의 연대기와 본질적 의미에 대해 연구하였고 상관관계를 분석했다. 그리고 지역화폐와 게이미피케이션 간의 효과적인 융합에 필요한 게이미피케이션 루프와 게임 메커닉스를 도출하였다. 게이미피케이션 은 게임요소와 게임적 사고를 활용하여 게임 이외의 특정분야의 문제를 해결하는 기술이다. 본 연구는 게이미피케이션을 적용하여 지역화폐의 확산과 발전이라는 문제의 해결의 단초를 찾으려는 노력의 일환이다. 이 연구를 시작으로 바람직한 블록체인 기반의 지혁화폐 모델을 게이미피케이션 원리과 개념을 반영하여 제시하고자 하였다. 이러한 블록체인 게이미피케이션 융합 연구가 지역 사회와 구성원 모두에게 실질적인 도움이되는 창의적인 현지 화폐 실현에 기여할 수 있길 기대한다.

PURPOSES : The accuracy of travel time information is a key measure of effectiveness and reliability of advanced traveler information systems. This study aims at investigating drivers' perception on the acceptable level of information accuracy and their corresponding valuations. METHODS : A questionnaire survey was executed for collecting driver perception data to capture the expectation level of travel time information provided and their willingness to pay for the information. A Tobit model was adopted for exploring the relationship among the acceptable level, driver socioeconomic characteristics and travel attributes. Since drivers' willingness to pay for accurate travel time information can be different according to their travel lengths, a piecewise linear regression model was developed to capture the sensitivity of values of travel time information to travel length. RESULTS : The analysis results suggest that trip purpose and travel time are two dominant factors to determine drivers' acceptable level of travel time information. For business and short trips, drivers want more accurate information than for non-business and long trips. Drivers' willingness to pay for travel time information also varies depending on their incomes, trip purposes and travel lengths. The results also show that drivers' valuation of travel time information provided is sensitive to their travel length. For longer trips, drivers are less sensitive to travel time information and then put less value on the information provided. CONCLUSIONS : Censored nonlinear regression models are developed to estimate drivers' acceptable accuracy for travel time information and their valuation using questionnaire survey data. The findings on drivers perception to the required accuracy of travel time information and their corresponding willingness to pay can be used in the design and deployment of advanced traveler information system to improve its effectiveness and usefulness through high compliance.

전자 화폐 및 전자 지불 시스템은 현재의 사회적 인프라(법, 제도, 문화, 노동, 금융, 기술)등의 총체적인 인프라이며, 응용산업이다. 인류의 화폐 문화 기술을 근본적으로 바꾸어 놓을 수 있는 역사적. 기술적 발전의 계기가 되고 있다. 이러한 산업을 이끌어가고 있는 최 첨병들의 기술과 동향 향후의 발전 추이에 대한 고찰을 하고자 한다.

Recently, it is increase in the processing of organic waste using anaerobic digestion. Therefore, the studies on theprocessing method for increasing the anaerobic digestion waste water. Among them, purification treatment and liquidfertilizer have been issues. But, efficiency of the ways is very insufficient. While increasing the anaerobic digestionprocess, increasing study for the treatment of anaerobic digestion waste water. In this study, It was evaluate thecharacteristic of liquid fertilizer of anaerobic digestion waste water as affected by aeration rate and solid-liquid separation.Also, we review liquid fertilizer degree using maturity assessment indicators of liquid fertilizer by national institute ofanimal science. As a result, insertion of air(3.5L/min) sample after solid-liquid separation appeared was the highest. pHand color was difficult to understanding correlation with liquid fertilizer among maturity assessment indicators(seedgermination, ammonia(NH3), hydrogen sulfide(H2S), electrical conductivity(EC), pH, color, etc).