• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.53-69
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• 2024

This study was attempted to promote changes in traditional design education at a time when non-face-to-face education is necessary, such as digitalization, rapid social structure change, and post-COVID-19 situations, and to meet the demands of the times when user-centered design is more emphasized. Therefore, the goal of this study is to explore the process of developing mobile apps and operating education for deaf learners through design thinking-based interaction design education for design students. Specifically, the curriculum was designed by selecting a design thinking methodology suitable for major students to experience empathy and solutions to user problems. The subject that students majoring in this study want to sympathize with is deaf learners, and the subject of the curriculum is the development of educational support applications for deaf learners. Accordingly, a mobile app prototype that can support online learning for deaf learners was created based on the interaction design education plan designed based on design thinking. In addition, after collecting and analyzing the feedback of deaf learners to evaluate the prototype effectiveness, the final mobile app prototype was presented as an output. Through this process, interactive design education based on design thinking helped to strengthen the ability to empathize with and solve the needs of deaf learners to major students and improve the design learning experience. Based on these findings, if a methodology suitable for various user groups is selectively accepted for design education, the students in the major will have the ability to design by prioritizing the actual needs of users despite changes in the environment of the future design society.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.5
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• pp.574-583
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• 2021

Predicting shipping markets is an important issue. Such predictions form the basis for decisions on investment methods, fleet formation methods, freight rates, etc., which greatly affect the profits and survival of a company. To this end, in this study, we propose a shipping freight rate prediction model for container ships using gated recurrent units (GRUs) and long short-term memory structure. The target of our freight rate prediction is the China Container Freight Index (CCFI), and CCFI data from March 2003 to May 2020 were used for training. The CCFI after June 2020 was first predicted according to each model and then compared and analyzed with the actual CCFI. For the experimental model, a total of six models were designed according to the hyperparameter settings. Additionally, the ARIMA model was included in the experiment for performance comparison with the traditional analysis method. The optimal model was selected based on two evaluation methods. The first evaluation method selects the model with the smallest average value of the root mean square error (RMSE) obtained by repeating each model 10 times. The second method selects the model with the lowest RMSE in all experiments. The experimental results revealed not only the improved accuracy of the deep learning model compared to the traditional time series prediction model, ARIMA, but also the contribution in enhancing the risk management ability of freight fluctuations through deep learning models. On the contrary, in the event of sudden changes in freight owing to the effects of external factors such as the Covid-19 pandemic, the accuracy of the forecasting model reduced. The GRU1 model recorded the lowest RMSE (69.55, 49.35) in both evaluation methods, and it was selected as the optimal model.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.297-302
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• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Knowledge Management Research
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• v.22 no.2
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• pp.19-32
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• 2021

Bitcoin, a representative cryptocurrency, is receiving a lot of attention around the world, and the price of Bitcoin shows high volatility. High volatility is a risk factor for investors and causes social problems caused by reckless investment. Since the price of Bitcoin responds quickly to changes in the world environment, we propose to predict the price volatility of Bitcoin by utilizing news information that provides a variety of information in real-time. In other words, positive news stimulates investor sentiment and negative news weakens investor sentiment. Therefore, in this study, sentiment information of news and deep learning were applied to predict the change in Bitcoin yield. A single predictive model of logit, artificial neural network, SVM, and LSTM was built, and an integrated model was proposed as a method to improve predictive performance. As a result of comparing the performance of the prediction model built on the historical price information and the prediction model reflecting the sentiment information of the news, it was found that the integrated model based on the sentiment information of the news was the best. This study will be able to prevent reckless investment and provide useful information to investors to make wise investments through a predictive model.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.1-11
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• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.835-844
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• 2024

The rapid expansion of the real-time streaming industry is driven by the widespread adoption of portable devices and the growth of video platforms. Consequently, the demand for transmitting and receiving large volumes of content has increased, leading to traffic congestion and inefficiency in traditional IP address-based networks. To address these issues, Contents Centric Networking (CCN) is being researched as an alternative. CCN is a network architecture based on content names (what) rather than IP addresses (where), where each node has a cache space called Content Store (CS) to alleviate server bottlenecks and traffic congestion. However, in a CCN environment, the departure of intermediate nodes between clients and servers can lead to packet loss and degradation of service quality. Therefore, research on managing the departure of intermediate nodes in real-time environments is essential. This study proposes a new method for detecting the departure of intermediate nodes through RSSI (Received Signal Strength Indicator) monitoring and for efficiently creating backup paths.

• Trans-
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• v.9
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• pp.67-92
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• 2020

The development of social platforms and digital technology has promoted the age of the communication in our society. As online communication has become commonplace, expressing feelings, thoughts and experiences on the Internet has become an everyday routine. Among them, SNS is one of the representative platforms for expressing oneself easily and interacting with other users. The way of communicating with the SNS about what they did and what experiences they experienced from one's everyday lives became more common. As a result, the museum makes various efforts to enhance visitors' attention and interest with the use of SNS. It provides a content-based programs and museum environment that allow visitors to enjoy playing and learning at the same time. This study will explore not only a simple appreciation, but also the way of communicating to everyday life in terms of the changes for museum environment through the development, implementation and adaptations of digital technology. Through this, mobile-based communication with SNS provides various values and quality of museum visit, can be completed with meaningful museum experience, and various roles and functions of the museum are examined in terms of social platform of experience.

• Journal of Gifted/Talented Education
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• v.19 no.1
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• pp.95-117
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• 2009

This study investigated the relationships of the self-regulated learning strategies used in both science and English classes and motivation to academic performance of science-gifted high school students. Participants of this study were 144 freshmen of Korea Science Academy It was found out that the use of self-regulation learning strategies and motivation exerts differential influence on the academic performance of science-gifted students, depending on the subjects they study. Results showed that they used more vigorously in science class those self-regulated strategies which consist of cognition, metacognition, and resource management strategies than in English class. In addition, their motivation level in science class was significantly higher than that in English class. Self-regulated strategies did not explain any variance in physics GPA. Task value among the motivation variables accounted for 2 percent of variance in physics GPA. Metacognition and time and study environment variables explained 8 percent and 15 percent of variance in English GPA, respectively. Self-efficacy in motivation accounted for 30 percent of variance in English GPA, These results were discussed in the light of instruction for science-gifted high students.