• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.203-211
• /
• 2024

Utilizing programming languages for data visualization can enhance the efficiency and effectiveness in handling data volume, processing time, and flexibility. However, practice is required to become proficient in programming. Therefore public data-based the problem bank was developed to practice data visualization in a programming automatic assessment system. Public data were collected based on topics suggested in the curriculum and were preprocessed to make it suitable for users to visualize. The problem bank was associated with the mathematics curriculum to learn various data visualization methods. The developed problems were reviewed to expert and pilot testing, which validated the level of the questions and the potential of integrating data visualization in math education. However, feedback indicated a lack of student interest in the topics, leading us to develop additional questions using student-center data. The developed problem bank is expected to be used when students who have learned Python in primary school information gifted or middle school or higher learn data visualization.

• Journal of Korean Academy of Fundamentals of Nursing
• /
• v.6 no.3
• /
• pp.570-582
• /
• 1999

Although computers cannot analyze textual data in the same way as they analyze numerical data. they can nevertheless be of great assistance to qualitative researchers. Thus, the use of computers in analyzing qualitative data has increased since the 1980s. The purpose of this article was to explore advantages and disadvanteges of using computers to analyze textual data and to suggest strategies to prevent problems of using computers. In additon, it illustrated characteristics and functions of softwares designed to analyze qualitative data to help researchers choose the program wisely. It also demonstrated precise functions and procedures of the NUDIST program which was designed to develop a conceptual framework or grounded theory from unstructured data. Major advantage of using computers in qualitative research is the management of huge amount of unstructured data. By managing overloaded data, researcher can keep track of the emerging ideas, arguments and theoretical concepts and can organize these tasks mope efficiently than the traditional method of 'cut-and-paste' technique. Additional advantages are the abilities to increase trustworthiness of research, transparency of research process, and intuitional creativity of the researcher, and to facilitate team and secondary research. On the other hand, disvantages of using computers were identified as worries that the machine could conquer the human understanding and as probability of these problems. it suggested strategies such as 1) deep understanding of orthodoxy in analytical process. To overcome philosophical and theoretical background of qualitative research method, 2) deep understanding of the data as a whole before using software, 3) use of software after familiarity with it, 4) continuous evaluation of software and feedback from them, and 5) continuous awareness of the limitation of the machine, that is computer, in the interpretive analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.5
• /
• pp.495-507
• /
• 2019

Data sparsity is one of the main challenges for the recommender system. The recommender system contains massive data in which only a small part is the observed data and the others are missing data. Most studies assume that missing data is randomly missing from the dataset. Therefore, they only use observed data to train recommendation model, then recommend items to users. In actual case, however, missing data do not lost randomly. In our research, treat these missing data as negative examples of users' interest. Three sample methods are seamlessly integrated into SVD++ algorithm and then propose SVD++_W, SVD++_R and SVD++_KNN algorithm. Experimental results show that proposed sample methods effectively improve the precision in Top-N recommendation over the baseline algorithms. Among the three improved algorithms, SVD++_KNN has the best performance, which shows that the KNN sample method is a more effective way to extract the negative examples of the users' interest.

• Journal of Wetlands Research
• /
• v.23 no.4
• /
• pp.277-286
• /
• 2021

This study compares the long-term hydrological cycles of the Seolmacheon and Cheongmicheon basin by applying the structural equation model (SEM). These two basins are found different especially in their land-use pattern. Both basins have the actual evapotranspiration data measured by the eddy-covariance method as well as the rainfall and runoff data. The length of the data considered in this study is nine years from 2010 to 2018. The structure of the SEM is determined by considering the correlations among the data as well as the general knowledge on the hydrological cycle. As a result, a total of three SEMs are applied sequentially to analyze their fittings. As irony would have it, two basins are found to be similar in the application of one SEM, but different in the application of another. Especially, when considering the feedback process between precipitation and evapotranspiration, two basins are found to be very different. That is, the feedback process between precipitation and evapotranspiration is found to be significant in the Cheongmicheon basin where the portion of agricultural area (i.e., paddy) is more than 40%.

• Journal of the Society of Disaster Information
• /
• v.17 no.3
• /
• pp.427-441
• /
• 2021

Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.6
• /
• pp.185-190
• /
• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.631-634
• /
• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• The Journal of the Korea Contents Association
• /
• v.1 no.1
• /
• pp.100-108
• /
• 2001

To develope more competitive products, lots of attentions are paid to the study of user participation in the process of product design. In this circumstance, the user participation at a usability testing phase is relatively active. However, the usability testing in the controlled laboratory has some drawbacks. for stance, It needs a lot of money, efforts, and time. Most of all, it is difficult to collect a vast range of data and gain a fast feedback Recently, to overcome these difficulties mentioned above, international usability testing has been applied. Especially, usability testing Contents through Internet is considered most effective one. Because it has a lot of merits such as easiness of collecting remote user, simplicity of collecting data, cost effectiveness and so on. With these merits, this study takes advantage of using a WWW to carry out usability test and to collect remote usability data. And a website was development for posting computer simulated product so that the remote users can test its usability through internet.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.3
• /
• pp.1406-1424
• /
• 2017

Recent advancements in network infrastructures provide increased opportunities to support data delivery over multiple paths. Compared with multi-homing scenario, overlay network is regarded as an effective way to construct multiple paths between end devices without any change on the underlying network. Exploiting multipath characteristics has been explored for TCP with multi-homing device, but the corresponding exploration with overlay network has not been studied in detail yet. Motivated by improving quality of experience (QoE) for reliable data delivery, we propose a multipath message transport protocol based on application level relay (MPMTP-AR). MPMTP-AR proposes mechanisms and algorithms to support basic operations of multipath transmission. Dynamic feedback provides a foundation to distribute reasonable load to each path. Common source decrease (CSD) takes the load weight of the path with congestion into consideration to adjust congestion window. MPMTP-AR uses two-level sending buffer to ensure independence between paths and utilizes two-level receiving buffer to improve queuing performance. Finally, the MPMTP-AR is implemented on the Linux platform and evaluated by comprehensive experiments.

• Journal of Communications and Networks
• /
• v.10 no.2
• /
• pp.148-155
• /
• 2008

In wireless data communication systems, retransmission of an erroneous packet is inevitable due to the harsh communication environment. In this paper, an efficient retransmission scheme using cooperation from neighboring nodes is investigated. In the cooperative retransmission scheme, an erroneous packet is transmitted to the destination by cooperative nodes which have favorable channels. This cooperative retransmission scheme requires no a priori information of neighboring nodes and has no limitation on the number of cooperating nodes. Distributed beamforming is used to accommodate multiple cooperating nodes. Phase and frequency offsets of cooperating signals are extracted from the NACK message and used to co-phase retransmitted data packets. The outage probability of the cooperative retransmission scheme is analyzed for the case of perfect synchronization and when the offsets are estimated. To reduce the impact of the residual phase and frequency offsets in cooperating signals, a low-rate feedback scheme is also investigated. It is shown that improved outage probability and reduced packet error rate (PER) performance can be achieved even for long data packets. The proposed cooperative retransmission scheme is found to outperform simple retransmission by the source as well as decode-and-forward cooperation.