• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• Journal of Information Technology Applications and Management
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• v.26 no.4
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• pp.1-18
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• 2019

The virtual reality (VR) is an immerging technology used in the serious games industry to treat psychological disorders like dementia. We created a system named as Virtual Reality Home (VRH) for the elderly who lived with Alzheimer's disease (or other form of dementia) and cognitive impairment using virtual reality technology. The purpose of our study is to measure the long-time immersion and retention of VRH on the moods and apathy, enhancement in physical and brain stimulation as well as a decision making with peoples of dementia and explore the experience of aged care home staff's member. The VRH shows a positive impact on the elderly participants and staff members. During the VRH experience, excitement and a great level of alertness were observed among the participants but few of them were feeling anxiety. Furthermore, we observed the improvement in physical, memory and brain stimulation, but the participants have a low focus on decision making because they wanted to explore all interactable objects in the VRH. This study suggests that the VR may have the potential to improve the quality of life, and these results can assist to expand the future development in the enhancement of efficiency of people with dementia.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.57 no.4
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• pp.479-488
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• 2024

In recent years, changes in the fishing ground environment have led to reduced catches by fishermen at traditional fishing spots and increased operational costs related to vessel exploration, fuel, and labor. In this study, we developed a deep learning model to classify the fishing activities of drift gillnet fishing boats using AIS (automatic identification system) trajectory data. The proposed model integrates long short-term memory and 1-dimensional convolutional neural network layers to effectively distinguish between fishing (throwing and hauling) and non-fishing operations. Training on a dataset derived from AIS and validation against a subset of CCTV footage, the model achieved high accuracy, with a classification accuracy of 90% for fishing events. These results show that the model can be used effectively to monitor and manage fishing activities in coastal waters in real time.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.545-554
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• 2006

RFID could be applied in the various fields such as distribution beside, circulation, traffic and environment on information communication outside. So this can speak as point of ubiquitous computing's next generation technology. However, it is discussed problem of RFID security recently, so we must prepare thoroughly about RFID security for secure information. In this paper, we proposed a method which could protect private information and ensure RFID's identification effectively storing face feature information on RFID tag. Our method which is improved linear discriminant analysis has reduced dimension of feature information which has large size of data. Therefore, we can sore face feature information in small memory field of RFID tag. Our propose d algorithm has shown 92% recognition rate in experimental results and can be applied to entrance control management system, digital identification card and others.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.32-43
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• 2013

These days, there are more and more IT research institutions which foresee cloud services as the predominant IT service in the near future and there, in fact, are actual cloud services provided by some IT leading vendors. Regardless of physical location of the service and environment of the system, cloud service can provide users with storage services, usage of data and software. On the other hand, cloud service has challenges as well. Even though cloud service has its edge in terms of the extent to which the IT resource can be freely utilized regardless of the confinement of hardware, the availability is another problem to be solved. Hence, this paper is dedicated to tackle the aforementioned issues; prerequisites of cloud computing for distributed file system, open source based Hadoop distributed file system, in-memory database technology and high availability database system. Also the author tries to body out the high availability mass distributed data management architecture in cloud service's perspective using currently used distributed file system in cloud computing market.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.315-323
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• 2001

The concept of vertical partitioning has been discussed so far in an objective of improving the performance of query execution and system throughput. It can be applied to the areas where the match between data and queries affects performance, which includes partitioning of individual files in centralized environments, data distribution in distributed databases, dividing data among different levels of memory hierarchies, and so on. In general, a vertical partitioning algorithm should support n-ary partitioning as well as a globally optimal solution for the generation of all meaningful fragments. Most previous methods, however, have some limitations to support both of them efficiently. Because the vertical partitioning problem basically includes the fuzziness property, the proper management is required for the fuzziness problem. In this paper we propose an efficient vertical $\alpha$-partitioning algorithm which is based on the fuzzy theory. The method can not only generate all meaningful fragments but also support n-ary partitioning without any complex mathematical computations.

• Proceedings of the IEEK Conference
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• summer
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• pp.204-208
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• 2004

Embedded business will be expanded market more and more since customers seek more wearable and ubiquitous systems. Cellular telephones, PDAs, notebooks and portable multimedia devices could bring higher microprocessor revenues and more rewarding improvements in performance and functions. Increasing battery capacity is still creeping along the roadmap. Until a small practical fuel cell becomes available, microprocessor developers must come up with power-reduction methods. According to MPR 2003, the instruction and data caches of ARM920T processor consume $44\%$ of total processor power. The rest of it is split into the power consumptions of the integer core, memory management units, bus interface unit and other essential CPU circuitry. And the relationships among CPU, peripherals and caches may change in the future. The processor working on higher operating frequency will exact larger cache RAM and consume more energy. In this paper, we propose advanced low power trace cache which caches traces of the dynamic instruction stream, and reduces cache access times. And we evaluate the performance of the trace cache and estimate the power of the trace cache, which is compared with conventional cache.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.719-740
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• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.726-734
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• 2015

Relational databases used by structuralizing data are the most widely used in data management at present. However, in relational databases, service becomes slower as the amount of data increases because of constraints in the reading and writing operations to save or query data. Furthermore, when a new task is added, the database grows and, consequently, requires additional infrastructure, such as parallel configuration of hardware, CPU, memory, and network, to support smooth operation. In this paper, in order to improve the web information services that are slowing down due to increase of data in the relational databases, we implemented a model to extract a large amount of data quickly and safely for users by processing Hadoop Distributed File System (HDFS) files after sending data to HDFSs and unifying and reconstructing the data. We implemented our model in a Web-based civil affairs system that stores image files, which is irregular data processing. Our proposed system's data processing was found to be 0.4 sec faster than that of a relational database system. Thus, we found that it is possible to support Web information services with a Hadoop-based big data processing technique in order to process a large amount of data, as in conventional relational databases. Furthermore, since Hadoop is open source, our model has the advantage of reducing software costs. The proposed system is expected to be used as a model for Web services that provide fast information processing for organizations that require efficient processing of big data because of the increase in the size of conventional relational databases.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.119-128
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• 2010

Computing environment in these days aim for user-oriented development called RIA (Rich Internet Application). As a representative development method of RIA, Flex Framework overcomes the weaknesses of the Mainframe and C/S (Client/Server). However, the issues, such as, difficulties in memory management, complexity of the binding structure and large capacities of the compile outputs are left to be solved. The purpose of this paper is to implement the framework which enables the fast and accurate development of user-oriented web application on the Flex platform. DDX (Dynamic Data eXchange) framework proposes standardized and efficient development environment in a Flex platform. And by using scalability-prepared library that is applicable for various job areas, the framework enhances the performance, increase development productivity and help construct stable system.