• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.857-862
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• 2021

Recently, rapid climate change has had a significant impact on the bee ecosystem. The decrease in the number of bees and the change in the flowering period have a huge impact on the harvesting of beekeepers. Accordingly, attention is focused on smart beekeeping, which introduces IoT technology to beekeeping. According to the characteristics of beekeeping, it is impossible to continuously observe the beehive in the hive with the naked eye, and the condition of the hive is mostly dependent on knowledge from experience. Although a system that can measure partly through sensors such as temperature/humidity change inside the hive and measurement of the amount of CO2 is applied, there is no research on measuring the movement path and amount of movement of bees inside the beehive. Part of the migration of honeybees inside the hive can provide basic information to predict the most important cleavage time in beekeeping. In this study, we propose a device that detects the movement path of bees and measures and records data entering and exiting the hive in real time. The device proposed in this study was developed according to the honeycomb standard of the existing beehive so that beekeeping farms could use it. The development method used a photodetector that can detect the movement of bees to configure 16 movement paths and to detect the movement of bees in real time. If the measured honeybee movement status is utilized, the problem of directly observing the colony with the naked eye in order not to miss the swarming time can be solved.

• The Korea Beekeeping Bulletin
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• s.331
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• pp.18-23
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• 2008

강원도 고랭지 지역과 서해안 해안이나 도서 지역에서만 6월 초순경까지 일부 아카시아 꽃이 남아 있어 유밀이되는데 기온이 높을 때는 수분도 많고 꿀 분비도 잘 안되어 밀원으로서의 가치가 별로 없게 된다. 아카시아 채밀도 끝나고 밤꿀 이나 화분, 로얄제리 생산이나 분봉 등을 시키는 시기가 되겠다. 6월에 개화되는 꽃으로는 밤나무, 대추나무, 흑싸리, 찔레, 다래 등이 주요 밀원인데 그 중 밤나무, 대추나무에서는 채밀 할 수 있으나 찔레, 다래등은 화분만으로 만족해야 할 것이다. 만일 밤, 대추에서도 장마가 일찍 오면 채밀은 미지수이다. 이 장마기가 양봉에 있어서 월동 다음으로 어려운 월 하기라 생각한다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.697-703
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• 2022

Due to the recent economic downturn caused by Covid-19 and the unstable international situation, many investors are choosing the derivatives market as a means of investment. However, the derivatives market has a greater risk than the stock market, and research on the market of market participants is insufficient. Recently, with the development of artificial intelligence, machine learning has been widely used in the derivatives market. In this paper, reinforcement learning, one of the machine learning techniques, is applied to analyze the scalping technique that trades futures in minutes. The data set consists of 21 attributes using the closing price, moving average line, and Bollinger band indicators of 1 minute and 3 minute data for 6 months by selecting 4 products among futures products traded at trading firm. In the experiment, DNN artificial neural network model and three reinforcement learning algorithms, namely, DQN (Deep Q-Network), A2C (Advantage Actor Critic), and A3C (Asynchronous A2C) were used, and they were trained and verified through learning data set and test data set. For scalping, the agent chooses one of the actions of buying and selling, and the ratio of the portfolio value according to the action result is rewarded. Experiment results show that the energy sector products such as Heating Oil and Crude Oil yield relatively high cumulative returns compared to the index sector products such as Mini Russell 2000 and Hang Seng Index.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.37-47
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• 2011

Stock price index option market has various investment strategies that have been developed. Specially, arbitrage strategies are very important to be efficient in option market. The purpose of this study is to improve profit using rough set and Box spread by using past option trading data. Option trading data was based on an actual stock exchange market tick data ranging from 2001 to 2006. Validation process was carried out by transferring the tick data into one-minute intervals. Box spread arbitrage strategies is low risk but low profit. It can be accomplished by back-testing of the existing strategy of the past data and by using rough set, which limit the time line of dealing. This study can make more stable profits with lower risk if control the strategy that can produces a higher profit module compared to that of the same level of risk.

• Journal of the Korean Data and Information Science Society
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• v.20 no.6
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• pp.1061-1073
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• 2009

Stock price index option investing is a scientific investment method and various index and investment strategies have been developed. The purpose of this study is to apply the variety of option investment strategies that have been introduced in the market and validate them using past option trading data. Option data was based on an actual stock exchange market tick data ranging from September 2001 to January 2007. Visual Basic is used to propose an option back-testing model. Validation process was carried out by transferring the tick data into ten-minute intervals and empirically analyzed. Furthermore, most option-related strategies have been applied to the model, and the usefulness of each strategies can be easily evaluated. As option investment has high leverage followed by high risks and profit, the optimal option investment strategy should be used according to the market condition at the time to make stable profit with minimum risk.

• Current Research on Agriculture and Life Sciences
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• v.7
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• pp.99-107
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• 1989

The study was conducted to obtain some basic information to establish the system of performance-tests and selection of honeybee queens(Apis mellifera) under Korean circumstances, Colony performances were tested with thirty colonies of Apis mellifera at two apiaries in Taegu, Korea from September, 1988 to August 1989. The results of performance-testing on the colonies are summarized as follows : The colony weight measured before wintering was averaged $23.6{\pm}1.90kg$ and the colony weight was decreased by $2.9{\pm}0.82kg$ in average during winter season. Thirteen colonies were entered in two story hive from thirty single box colonies from April 17 to May 5, 1989 with increase of bee population and, consequently, the ability of enter-supers of the colonies apperared to be low. The ability of collecting pollen was measured to be $14.8{\pm}2.15gr$ per colony during 24 hours in April, and the number of swarm cells was counted $12.5{\pm}3.43$ cells per colony in aveage. Tendency to use propolis appeared to be moderate, and the number of returning foragers for a minute per colony was counted $108.7{\pm}18.31$ bees in average. Brood area was measured $2,464{\pm}628,67cm^2$ per colony in the post nectar flow season of acasia, and 30.8 percent of the colonies appeared to be infected with chalkbrood disease, The amount of honey production was $14.9{\pm}8.49kg$ per colony, which was harvested two times during the main nectar flow season of acasia.