• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.102-105
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• 2003

"e-Leaning" system is classified by lecture time into two types, that is, "synchronous type" spent the same lecture time between the lecturer and students, and "asynchronous type" spent the different lecture time. The size of image database is huge, and there are some problem on the management of the lecture image database in "asynchronous type" e-Learning system. The one of them is that the time tag for the database management must be added manually at present, and the cost of the addition of the time tag causes a serious problem. To resolve the problem, we will use the character recognition for the characters written by the lecturer on whiteboard, and will add the recognized character as a keyword to the tag of the image database. If the database would have the keyword, we could retrieve the database by the keyword efficiently, and the student could select the interested lecture scene only in the full lecture database.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.807-814
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• 2021

As economic activities decrease, and the stock market decline due to COVID-19, many people are jumping into stock investment as an alternative source of income. As people's interest increases, many stock price analysis studies are underway to earn more profits. Due to the variance observed in the stock markets, it is necessary to analyze each stock independently and consistently. To solve this problem, we designed and implemented models and services that analyze stock prices using a reinforcement learning technique called Asynchronous Advantage Actor-Critic(A3C). Stock market data reflected external factors such as government bonds and KOSPI (Korea Composite Stock Price Index) as well as stock prices. Our proposed work provides a web service with a visual representation of predictions of stocks and stock information through which directions are given to investors to make safe investments without analyzing domestic and foreign stock market trends.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2985-2995
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• 1997

In this paper, we proposed the "synchronous & asynchronous distance education system" which is able to interact among teachers and students for open education in cyberspace, and it is based telecommunication technology and multimedia technology. Specially, This system gets rid of the nufamiliarity and inconvenient feeling during the distance education. Also it supports the mediation of floor mode, for a group lecture and supports the synchronous mode for face-to-face effective and asynchronous mode for self-learning. The asynchronous mode has the down load function and the consultant mode (between teacher and student). The element technologies of this system consists of application sharing technique, whiteboard, various video window display, audio support, user interface, environment setup, session management, access control, network control and media control for collaborative distance education.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.225-232
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• 2024

The Asynchronous indivisual learning offer advantages such as allowing learners to study at their preferred times without spatial constraints. However, since these classes are not conducted in real-time, there are limitations in conveying learners' feedback on problematic or inadequately explained course content to the instructors. This paper proposed a system for relaying feedback information from learners who view course content to the instructors. Learners can investigate the reasons for pausing online recorded class content, and they can transmit these pause reasons along with the time information of the paused content to the instructors. Instructors receive feedback information and pause times of learners' online recorded class videos in graphical form, making it easier to identify areas with numerous issues in the course content at a glance. Instructors can incorporate this feedback to re-upload the content, resulting in higher-quality course materials, which, in turn, can enhance learners' academic achievements.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.297-307
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• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.

• 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 information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• The Journal of Korean Association of Computer Education
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• v.10 no.4
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• pp.93-102
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• 2007

The present study is to investigate the elements of e-learning strategies affecting the levels of participation, achievement and satisfaction for learners who participated in the university blended learning environment. For this, 58 subjects were recruited who participated in the blended learning class at K university. E-learning strategies, achievement and satisfaction levels were measured for data collection, and the level of participation was measured by analyzing the LMS log-in database. For data analysis, first, means and standard deviation were computed to find the level of e-learning strategies of the subjects. Second, linear regression analysis was conducted to find the e-learning strategies that could estimate the levels of achievement, participation and satisfaction. As a result, variables to estimate the achievement level included time management strategy and overload management strategy. Variables to estimate the participation level included self-directed strategy, time management strategy and overload management strategy. Finally, variables to estimate the satisfaction level included multiple discussion management strategy, asynchronous management strategy and sociality. Based on these estimated variables, the author suggested some ideas to increase the educational effectiveness.