• 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.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.3-12
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• 2022

Image deblurring aims to remove image blur, which can be generated while shooting the pictures by the movement of objects, camera shake, blurring of focus, and so forth. With the rise in popularity of smartphones, it is common to carry portable digital cameras daily, so image deblurring techniques have become more significant recently. Originally, image deblurring techniques have been studied using traditional optimization techniques. Then with the recent attention on deep learning, deblurring methods based on convolutional neural networks have been actively proposed. However, most of them have been developed while focusing on better performance. Therefore, it is not easy to use in real situations due to the speed of their algorithms. To tackle this problem, we propose a novel deep learning-based deblurring algorithm that can be operated in real-time on HD resolution. In addition, we improved the training and inference process and could increase the performance of our model without any significant effect on the speed and the speed without any significant effect on the performance. As a result, our algorithm achieves real-time performance by processing 33.74 frames per second at 1280×720 resolution. Furthermore, it shows excellent performance compared to its speed with a PSNR of 29.78 and SSIM of 0.9287 with the GoPro dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3C
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• pp.115-125
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by insitu test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network (NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. It was already found that NN model can come over the site dependency and prediction accuracy is greatly improved when compared with present theoretical and empirical models. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network (CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. Prediction results of CNN model are compared with those of conventional empirical and theoretical models and multi-layered neural network model, which has the optimized structure. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Korean Journal of Ecology and Environment
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• v.56 no.2
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• pp.140-150
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• 2023

The Hangang River^* is necessary to manage the water environment of severe pollution due to the high density of residential areas, parks, and agriculture and the large population concentrated there. Benthic macroinvertebrates, such as chironomids larvae, are bioindicator species that reflect environmental changes and are crucial for water quality monitoring. In this study, we investigated morphological characteristics and molecular analysis of the chironomids larvae inhabiting the Hangang River area for water environment surveys. For this research, 20 rivers, lakes, and urban area in the Hangang River basin were selected. Chironomids larvae were collected from July to September 2022, and their appearance and characteristics were identified through morphological identification. In addition, phylogenetic analysis was performed based on the mtCOI gene sequences of the collected chironomids larvae, and identification at the genus level was confirmed. As a result, 32 species and 18 genera of 3 subfamilies of Chironomidae larvae were identified, and Stictochironomus sp. dominated most sites(6 sites). The morphological characteristics of the identified chironomids larvae, such as the mentum, ventromental plate, and antenna, were organized into table and pictorial keys, and a Bayesian inference molecular phylogeny was presented. These results provide basic morphological information for genus-level identification and can be used as fundamental information for water quality management.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.95-103
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• 2023

This paper proposes an artificial intelligence convergence education program for teaching the main concept and principle of Support Vector Machines(SVM) at elementary schools. The developed program, based on Jeju's natural environment theme, explains the decision boundary and margin of SVM by vertical and parallel from 4th grade mathematics curriculum. As a result of applying the developed program to 3rd and 5th graders, most students intuitively inferred the location of the decision boundary. The overall performance accuracy and rate of reasonable inference of 5th graders were higher. However, in the self-evaluation of understanding, the average value was higher in the 3rd grade, contrary to the actual understanding. This was due to the fact that junior learners had a greater tendency to feel satisfaction and achievement. On the other hand, senior learners presented more meaningful post-class questions based on their motivation for further exploration. We would like to find effective ways for artificial intelligence convergence education for elementary school students.

• Convergence Security Journal
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• v.22 no.3
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• pp.25-32
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• 2022

Recently, the image processing industry has been activated as deep learning-based technology is introduced in the image recognition and detection field. With the development of deep learning technology, learning model vulnerabilities for adversarial attacks continue to be reported. However, studies on countermeasures against poisoning attacks that inject malicious data during learning are insufficient. The conventional countermeasure against poisoning attacks has a limitation in that it is necessary to perform a separate detection and removal operation by examining the training data each time. Therefore, in this paper, we propose a technique for reducing the attack success rate by applying modifications to the training data and inference data without a separate detection and removal process for the poison data. The One-shot kill poison attack, a clean label poison attack proposed in previous studies, was used as an attack model. The attack performance was confirmed by dividing it into a general attacker and an intelligent attacker according to the attacker's attack strategy. According to the experimental results, when the proposed defense mechanism is applied, the attack success rate can be reduced by up to 65% compared to the conventional method.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.33-40
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• 2009

The critical lag of crosswalk pedestrians is an important parameter in analyzing traffic operation at unsignalized crosswalks, however there is few research in this field in Korea. The purpose of this study is to develop a model to estimate the critical lag. Among the elements which influence the critical lag, the age of pedestrians and the length of crosswalks, which have fuzzy characteristics, and the each lag which is rejected or accepted are collected on crosswalks of which lengths range from 3.5 m to 10.5 m. The values of the critical lag range from 2.56 sec. to 5.56 sec. The age and the length are divided to the 3 fuzzy variables each, and the critical lag of each case is estimated according to Raff's technique, so a total of 9 fuzzy rules are established. Based on the rules, an ANFIS (Adaptive Neuro-Fuzzy Inference System) model to estimate the critical lag is built. The predictability of the model is evaluated comparing the observed with the estimated critical lags by the model. Statistics of $R^2$, MAE, MSE are 0.96, 0.097, 0.015 respectively. Therefore, the model is evaluated to explain the result well. During this study, it is found that the critical lag increases rapidly over the pedestrian's age of 40 years.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.383-392
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established. Therefore, uncertainty analysis are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an unexpected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

• The Journal of Korean Philosophical History
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• no.28
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• pp.155-182
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• 2010

When we divide Environmental Ethics education into the elements of 'knowledge - emotion - behavior', we need to focus on 'knowledge' at high school level. In general Moral Education, 'knowledge - emotion - behavior' is a circular link, but as Environmental Ethics is a matter of 'consciousness', it is desirable to instruct with the process of 'knowledge>emotion, behavior'. Teaching 'Consciousness on Nature' is not recommended at elementary or middle school level because it demands higher inference. On the contrary, considering the reality in high school it is not recommended to teach the necessity and method of recycling or to go field trip to the polluted area. Rather, it is better to inform the students of Environmental Ethics' viewpoints and let them know the ways of moral judgments. The view of nature in Orientalism is well explained through the Environmental Ethics' viewpoint. To explain the view of nature in Orientalism we should concentrate on the theory, not on the attitude of life. And we should rather compare the viewpoints of nature in Confucianism, Taoism and Buddhism respectively than explain in Orientalism all together. That is, if we compare with the viewpoints of Environmental Ethics and explain similarities & differences in Confucianism, Taoism and Buddhism, we can complement Environmental Ethics or present the third approach.