• Korean Journal of Organic Agriculture
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• v.27 no.1
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• pp.1-16
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• 2019

The questionnaire survey was conducted on 225 farmers in Gyeonggi-do, Jeollanam-do and Jeollabuk-do. A total of 189 (84%) farmers responded. 72% of the respondents were males, 50.3% were aged 60 or older, and 51.3% had less than 5 years of farming experience. 78.8% of the respondents are pesticide-free, and 44.4% of respondents have less than 0.5 ha of farming scale. 61.4% of the cultivated crops were vegetable crops. The order of seeds and seedlings to buy was tomato (23.3%), cucumber (12.2%) and pepper (10.6%). The cost of purchasing seeds ranged from a minimum of 100,000 won to a maximum of 5 million won. 78.3% of respondents answered that they well-knew or knew about organic seeds. 78.3% of respondents answered that they knew or knew about organic seeds. Of the positive effects of mandatory use of organic seeds, 41.3% of respondents said they would increase confidence in organic certification. However, 41% of respondents who opposed the mandatory use of organic seeds said that "The strengthening of regulations will make organic agriculture more difficult." When the use of organic seeds is mandatory, 43.4% of the respondents favor direct support for the purchase of organic seeds, which should be supported politically by the state. When organic seeds were supplied, the disease resistant seeds (53.4%) was the preferred characteristic of organic seeds. For the optimal price of organic seeds, 38.6% of respondents wanted the same price as the commercialized conventional seed. In this study, the questionnaire was conducted for three major organic farming regions, but many of the respondents were judged to have a legal position on the mandatory use of organic seeds. Therefore, the results of this study can be used as a basic data for reviewing the legislation on the organic seed production and distribution suitable for the situation of Korean organic farming.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.152-159
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• 2023

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.

• Journal of Bio-Environment Control
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• v.26 no.4
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• pp.258-267
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• 2017

The price competitiveness of photovoltaic system (PV system) has risen recently due to the growth of industries, however, it is rarely applied to the greenhouse compared to other renewable energy. In order to evaluate the application of PV system in the greenhouse, power generation and optimal installation area of PV panels should be analyzed. For this purpose, the prediction of the heating and cooling loads of the greenhouse is necessary at first. Therefore, periodic and maximum energy loads of a multi-span greenhouse were estimated using Building Energy Simulation(BES) and optimal installation area of PV panels was derived in this study. 5 parameter equivalent circuit model was applied to analyzed power generation of PV system under different installation angle and the optimal installation condition of the PV system was derived. As a result of the energy simulation, the average cooling load and heating load of the greenhouse were 627,516MJ and 1,652,050MJ respectively when the ventilation rate was $60AE{\cdot}hr^{-1}$. The highest electric power production of the PV system was generated when the installation angle was set to $30^{\circ}$. Also, adjustable PV system produced about 6% more electric power than the fixed PV system. Optimal installation area of the PV panels was derived with consideration of the estimated energy loads. As a result, optimal installation area of PV panels for fixed PV system and adjustable PV system were $521m^2$ and $494m^2$ respectively.

• Journal of Environmental Policy
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• v.8 no.4
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• pp.75-94
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• 2009

The process of obtaining third-party financing contacts was analyzed via a two-stage game model: a "signaling game" for the first stage,and a "principal-agent model" for the second stage. The two-stage game was solved by a process of backward induction. In the second stage game, the optimal effort level of the energy saving company (ESCO), the optimal compensation scheme of the energy user, and the optimal payoffs for both parties were derived for each subgame. The optimal solutions forthe different subgames were then compared with each other. Our main finding was that if there is some restriction on ESCO's revenue (e.g. a progressive sales tax) that causes ESCO's revenue toincrease at a decreasing rate, then the optimal sharing ratio is uniquely determined at a level of strictly less than one under a linear compensation scheme, i.e. a unique balance exists. Subgames have a unique equilibrium arrived at separately for each situation,. Within this equilibrium, energy users accept energy audit proposals from H-type ESCOs with high levels of technology, but reject proposals from L-type ESCOs with low levels of technology. While L-type ESCOs cannot attain profits in the third-party financing market, H-type ESCOS can pocket the price differential between L-type and H-type audit fees. Accordingly, revenues in an H-type ESCO equilibrium increase not only in line with the technology of the ESCO inquestion, but also faster than in an L-type equilibrium due to more advanced technology. At the same time, energy users receive some positive payoff by allowing ESCOs to perform third-party financing tasks within their existing energy system without incurring any extra costs.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.111-120
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• 2011

High-rise buildings have recently increased over the residential, commercial and office facilities, thus an understanding of construction cost for high-rise building projects has been a fundamental issue due to enormous construction cost as well as unpredictable market conditions and fluctuations in the rate of inflation by long-term construction periods of high-rise projects. Especially, recent violent fluctuations of construction material prices add to problems in construction cost forecasting. This research, therefore, develops a time-series model with the Box-Jenkins methodologies and material prices time-series data in Korea in order to forecast future trends of unit prices of required materials. BIM (Building Information Modeling) approaches are also used to analyze injection time of construction resources and to conduct quantity takeoff so that total material price can be forecasted. Comparative analysis of Predictability of tentative ARIMA (Autoregressive Integrated Moving Average) models was conducted to determine optimal time-series model for forecasting future price trends. Proposed BIM based time series forecasting model can help to deal with sudden changes in economic conditions by estimating future material prices.

• The Korean Journal of Franchise Management
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• v.13 no.2
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• pp.1-15
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• 2022

Purpose: The globalization of the Korean restaurant franchise industry differs from the business performance of enhancing the brand image and customers' intention to revisit depending on the degree of localization marketing. Therefore, it is necessary to consider the extent to which the localization marketing activities of overseas Korean restaurant franchise companies affect the customer's perception. This study aims to investigate the effects of localization marketing (Localized Menu, Localized Price, Localized Service Experience, Localized Promotion, Localized Physical Environment) of Korean restaurant franchise companies on customer revisit intention. Research design, data, and methodology: For this study, 150 questionnaires using local Korean restaurants in Beijing, China, were analyzed using SPSS Ver.21 and AMOS Ver.22. Result: It was confirmed that the localized menu, localized service experience, and localized physical environment all affect the intention to revisit customers. Based on these verification results, if overseas franchises fully recognize localization marketing, which is an important factor for local business success, and establish localization strategies, they can gain an edge in competition with local Korean restaurants or restaurant franchises founded by locals. There may be a higher probability that However, it was found that localization price and localization promotion had no mediating effect of brand image between revisit intention and revisit intention. It was found that it had no effect on the degree of inquiry and had a negative effect. Conclusions: Due to the impact of the COVID-19 pandemic, there have been many changes in the domestic and overseas food service industry over the past two years. Therefore, in future research, it is necessary to study the localization of overseas Korean restaurant franchise companies that are more multidimensionally subdivided. Various measures of customized localization marketing for optimal regional characteristics should be developed and applied to enhance customer revisiting and brand image of Korean restaurant franchise companies entering overseas. In the future, this study will be meaningful data for the establishment of localization marketing (Localized Menu, Localized Price, Localized Service Experience, Localized Promotion, Localized Physical Environment) strategies for Korean restaurant franchise companies that consider overseas expansion or have already entered.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• Journal of Distribution Research
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• v.17 no.4
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• pp.29-53
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• 2012

As so many marketers get to use diverse sales promotion methods, manufacturer and retailer in a channel often use them too. In this context, diverse issues on sales promotion management arise. One of them is the issue of unplanned buying. Consumers' unplanned buying is clearly better off for the retailer but not for manufacturer. This asymmetric influence of unplanned buying should be dealt with prudently because of its possibility of provocation of channel conflict. However, there have been scarce studies on the sales promotion management strategy considering the unplanned buying and its asymmetric effect on retailer and manufacturer. In this paper, we try to find a better way for a manufacturer in a channel to promote performance through the retailer's sales promotion efforts when there is potential of unplanned buying effect. We investigate via game-theoretic modeling what is the optimal cost sharing level between the manufacturer and retailer when there is unplanned buying effect. We investigated following issues about the topic as follows: (1) What structure of cost sharing mechanism should the manufacturer and retailer in a channel choose when unplanned buying effect is strong (or weak)? (2) How much payoff could the manufacturer and retailer in a channel get when unplanned buying effect is strong (or weak)? We focus on the impact of unplanned buying effect on the optimal cost sharing mechanism for sales promotions between a manufacturer and a retailer in a same channel. So we consider two players in the game, a manufacturer and a retailer who are interacting in a same distribution channel. The model is of complete information game type. In the model, the manufacturer is the Stackelberg leader and the retailer is the follower. Variables in the model are as following table. Manufacturer's objective function in the basic game is as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. And retailer's is as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+p_u(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. The model is of four stages in two periods. Stages of the game are as follows. (Stage 1) Manufacturer sets wholesale price of the first period($w_1$) and cost sharing level of channel sales promotion(${\Psi}$). (Stage 2) Retailer sets retail price of the focal brand($p_1$), the unplanned buying item($p_u$), and sales promotion level(L). (Stage 3) Manufacturer sets wholesale price of the second period($w_2$). (Stage 4) Retailer sets retail price of the second period($p_2$). Since the model is a kind of dynamic games, we try to find a subgame perfect equilibrium to derive some theoretical and managerial implications. In order to obtain the subgame perfect equilibrium, we use the backward induction method. In using backward induction approach, we solve the problems backward from stage 4 to stage 1. By completely knowing follower's optimal reaction to the leader's potential actions, we can fold the game tree backward. Equilibrium of each variable in the basic game is as following table. We conducted more analysis of additional game about diverse cost level of manufacturer. Manufacturer's objective function in the additional game is same with that of the basic game as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. But retailer's objective function is different from that of the basic game as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+(p_u-c)(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. Equilibrium of each variable in this additional game is as following table. Major findings of the current study are as follows: (1) As the unplanned buying effect gets stronger, manufacturer and retailer had better increase the cost for sales promotion. (2) As the unplanned buying effect gets stronger, manufacturer had better decrease the cost sharing portion of total cost for sales promotion. (3) Manufacturer's profit is increasing function of the unplanned buying effect. (4) All results of (1),(2),(3) are alleviated by the increase of retailer's procurement cost to acquire unplanned buying items. The authors discuss the implications of those results for the marketers in manufacturers or retailers. The current study firstly suggests some managerial implications for the manufacturer how to share the sales promotion cost with the retailer in a channel to the high or low level of the consumers' unplanned buying potential.

• The Journal of Information Technology
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• v.3 no.2
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• pp.73-85
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• 2000

The efficient algorithms are suggested in this study for solving the multicommodity network flow problems applied to Communications Systems. These problems are typical NP-complete optimization problems that require integer solution and in which the computational complexity increases numerically in appropriate with the problem size. Although the suggested algorithms are not absolutely optimal, they are developed for computationally efficient and produce near-optimal and primal integral solutions. We supplement the traditional Lagrangian method with a price-directive decomposition. It proceeded as follows. First, A primal heuristic from which good initial feasible solutions can be obtained is developed. Second, the dual is initialized using marginal values from the primal heuristic. Generally, the Lagrangian optimization is conducted from a naive dual solution which is set as ${\lambda}=0$. The dual optimization converged very slowly because these values have sort of gaps from the optimum. Better dual solutions improve the primal solution, and better primal bounds improve the step size used by the dual optimization. Third, a limitation that the Lagrangian decomposition approach has Is dealt with. Because this method is dual based, the solution need not converge to the optimal solution in the multicommodity network problem. So as to adjust relaxed solution to a feasible one, we made efficient re-allocation heuristic. In addition, the computational performances of various versions of the developed algorithms are compared and evaluated. First, commercial LP software, LINGO 4.0 extended version for LINDO system is utilized for the purpose of implementation that is robust and efficient. Tested problem sets are generated randomly Numerical results on randomly generated examples demonstrate that our algorithm is near-optimal (< 2% from the optimum) and has a quite computational efficiency.