• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.703-709
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• 2023

It is most desirable to train with a real crane in an environment similar to that of a port for crane operation training in charge of loading and unloading in a port, but it has time and space limitations and cost problems. In order to overcome these limitations, VR(Virtual Reality) based crane training programs and related devices are receiving a lot of attention. In this paper, we designed and implemented a VR-based harbor crane simulator operating on an HMD. The simulator developed in this paper consists of a crane simulator program that operates on the HMD, an IoT driving terminal that processes trainees' crane operation input, and a training server that stores trainees' training information. The simulator program provides VR-based crane training scenarios implemented with Unity3D, and the IoT driving terminal developed based on Arduino is composed of two controllers and transmits the user's driving operation to the HMD. In particular, the crane simulator in this paper uses a training server to create a database of environment setting values for each educator, progress and training time, and information on driving warning situations. Through the use of such a server, trainees can use the simulator in a more convenient environment and can expect improved educational effects by providing training information.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.37-51
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• 2023

The ground roll is the most common coherent noise in land seismic data and has an amplitude much larger than the reflection event we usually want to obtain. Therefore, ground roll suppression is a crucial step in seismic data processing. Several techniques, such as f-k filtering and curvelet transform, have been developed to suppress the ground roll. However, the existing methods still require improvements in suppression performance and efficiency. Various studies on the suppression of ground roll in seismic data have recently been conducted using deep learning methods developed for image processing. In this paper, we introduce three models (DnCNN (De-noiseCNN), pix2pix, and CycleGAN), based on convolutional neural network (CNN) or conditional generative adversarial network (cGAN), for ground roll suppression and explain them in detail through numerical examples. Common shot gathers from the same field were divided into training and test datasets to compare the algorithms. We trained the models using the training data and evaluated their performances using the test data. When training these models with field data, ground roll removed data are required; therefore, the ground roll is suppressed by f-k filtering and used as the ground-truth data. To evaluate the performance of the deep learning models and compare the training results, we utilized quantitative indicators such as the correlation coefficient and structural similarity index measure (SSIM) based on the similarity to the ground-truth data. The DnCNN model exhibited the best performance, and we confirmed that other models could also be applied to suppress the ground roll.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.21-38
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• 2023

The present study was designed to investigate the effect of the difficulty level of retrieval practice and the association strength of categories and stimuli within categories on memory inhibition. Most of the studies have investigated whether inhibition was occurred by manipulating the degree of association strength, emotion value or physical characteristics of non-retrieval practice words within the retrieval practice category. Therefore, it was necessary to study how inhibition occurs according to the degree of difficulty of retrieval stimuli during retrieval practice. The difficulty of retrieval was manipulated into three levels: difficult condition, normal condition, and easy condition through the degree of presentation of consonants and vowels of words during retrieval learning. Additionally, the strength of association between categories and words within categories was manipulated. In previous studies, retrieval-induced forgetting occurred under conditions where the association strength between categories and words within the categories was strong. On the other hand, retrieval-induced forgetting did not occur under conditions where the association strength between categories and words within the categories was weak. The present study, if the inhibition process differs according to the difficulty of retrieval, the possibility of different results from previous studies was explored according to the difference in the strength of association with the category. As a result of the study, in the condition of strong association strength, retrieval-induced forgetting was observed under normal and difficult retrieval difficulty conditions. Whereas retrieval-induced forgetting was not observed under conditions of easy retrieval difficulty condition. In the condition of weak association strength, retrieval-induced forgetting tended to occur under difficult retrieval difficulty conditions. Whereas retrieval-induced forgetting was not observed under conditions of normal and easy retrieval difficulty condition. These results suggest that memory inhibition may appear differently depending on the difficulty of retrieval.

• Journal of Science Education
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• v.47 no.1
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• pp.107-116
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• 2023

Concerns related to diversity are important throughout society, especially in the context of expansive globalization. In education, diversity-related issues require careful consideration to ensure social groups that have historically been marginalized benefit from educational opportunities. In this study, we investigated a case from within the United States aimed at addressing diversity issues in schools and discuss the implications of this study in relation to diversity issues in Korea. More specifically, we examined the features of the professional development program designed and implemented by the Smithsonian Science Education Center, as well as survey results from teachers who participated between 2019-2020. Our findings revealed that the program provided participants context specific experiences, space and time to develop an in-depth understandings of the causes of diversity issues and supports to attend to the various perspectives needed to set specific goals and action plans and to examine, refine, and revise their plans. Further, features of the professional development program had meaningful effects on participants' learning experience as they were supported to identify useful proposals and take action to solve their specific diversity issues. The findings from this study offer important implications for designing professional development and organizing supports to address varied current and future diversity issues in Korean school contexts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.83-89
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• 2023

The clustering technique using RF fingerprint extracts the characteristic signature of the transmitters which are embedded in the transmission waveforms. The output of the RF-Fingerprint feature extraction algorithm for clustering identical DMR(Digital Mobile Radios) is a high-dimensional feature, typically consisting of 512 or more dimensions. While such high-dimensional features may be effective for the classifiers, they are not suitable to be used as inputs for the clustering algorithms. Therefore, this paper proposes a dimension reduction algorithm that effectively reduces the dimensionality of the multidimensional RF-Fingerprint features while maintaining the fingerprinting characteristics of the DMRs. Additionally, it proposes a clustering algorithm that can effectively cluster the reduced dimensions. The proposed clustering algorithm reduces the multi-dimensional RF-Fingerprint features using t-SNE, based on KL Divergence, and performs clustering using Density Peaks Clustering (DPC). The performance analysis of the DMR clustering algorithm uses a dataset of 3000 samples collected from 10 Motorola XiR and 10 Wintech N-Series DMRs. The results of the RF-Fingerprinting-based clustering algorithm showed the formation of 20 clusters, and all performance metrics including Homogeneity, Completeness, and V-measure, demonstrated a performance of 99.4%.

• Journal of The Korean Association For Science Education
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• v.43 no.2
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• pp.111-124
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• 2023

The aim of this study was to explore science teachers' perceptions of good science teaching. To this end, the Korean Teaching Observation Protocol (KTOP), which was developed for the purpose of observing and improving science teaching, was utilized. In the first survey, teachers were asked whether they thought each item in the KTOP was important for good science teaching, the extent to which they implemented these items, and the level of difficulty in implementing them. The second survey asked teachers what they believed to be the reasons and solutions for the KTOP items that they had responded as difficult to implement. The responses obtained from 63 teachers in the first survey and 35 teachers in the second survey were categorized based on the characteristics of the responses. The categorized contents were then summarized and discussed for their features. As a result, science teachers responded that all items in KTOP, except for one, are important for good science teaching. However, it was also shown that the level of execution was low in cases where implementation was difficult. For the 13 KTOP items that were considered important but difficult to implement and showed relatively low implementation level, many respondents (69%) attributed the reason to both students and teachers. However, the most common response (60%) was that the teacher should solve those difficulties. From this, it was found that understanding and supporting teachers, as well as enhancing their competencies, are more important for good science teaching than external factors. We hope that this research findings will help to better understand the specific difficulties that science teachers face in their classes and contribute to practical efforts that aim to address these challenges.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.8
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• pp.327-342
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• 2017

In this paper, we introduce an integrated processing system of smart sensor data for IoT service which collects sensor data and efficiently processes it. Based on the technology of collecting sensor data to the development of the IoT field and sending it to the network · Based on the receiving technology, as various projects such as smart homes, autonomous running vehicles progress, the sensor data is processed and effectively An autonomous control system to utilize has been a problem. However, since the data type of the sensor for monitoring the autonomous control system varies according to the domain, a sensor data integration processing system applying the autonomous control system to various different domains is necessary. Therefore, in this paper, we introduce the Smart Sensor Data Integrated Processing System, apply it and use the window as a reference to process internal and external sensor data 1) receiveData, 2) parseData, 3) addToDatabase 3 With the process of the stage, we provide and implement the automatic window opening / closing system "Smart Window" which ventilates to create a comfortable indoor environment by autonomous control system. As a result, standby information is collected and monitored, and machine learning for performing statistical analysis and better autonomous control based on the stored data is made possible.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.63-72
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• 2017

This study made of centrally actual application in general arts classes based on movie in university. Especially, I analyzed the activities of the class with 'Untouchable: 1% friendship' among 6 films. The objects of this study are 44 students of D university in Busan Metropolitan City who take 'creative fusion from movie' general arts class which opened first semester in 2016. In this study, students were able to watch movies through the creative class, which was out of the traditional classroom method, and after 15 hours of learning the quiz online, they conducted 15 weeks as a teaching method to perform tasks, presentations, experiments, and experiences in regular class time. The results of this study are as follows. 'It is a general arts class that makes movements live,' 'It is a general arts class that shows movies from various perspectives,' and 'It is a general arts class that makes me know.' This suggests that the educational medium, which is easily accessible in everyday life, and the general arts class, which is active in the space outside the framework, are perceived as stimulating curiosity and adding fun to college students.