• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1779-1786
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• 2014

Artificial Intelligence-based techniques have been applied to problems where mathematical relations can not be presented due to complicatedness of the physical process. A representative example in hydraulics is the local scour around bridge piers. This study presents a GEP model for predicting the local scour around bridge piers. The model is trained by 64 laboratory data to build the regression equation, and the constructed model is verified against 33 laboratory data. Comparisons between the models with dimensional and normalized variables reveals that the GEP model with dimensional variables predicts better. The proposed model is now applied to two field datasets. It is found that the MAPE of the scour depths predicted by the GEP model increases compared with the predictions of local scours in laboratory scale. In addition, the model performance increases significantly when the model is trained by the field dataset rather than the laboratory dataset. The findings suggest that apart from the ANN model, GEP model is a sound and reliable model for predicting local scour depth.

• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.331-339
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• 2016

According to high-rise, complexation, and enlargement of buildings, various construction methods are being developed, and the significance of construction method selection about main work types has emerged as a major interest. However, it has been pointed out that hand-on workers cannot consider project characteristics carefully, and they lack an objective standard or reference for main construction method selection. Hence, the selection is being made depending on hand-on workers' experience and intuition. To solve this problem, various studies have proceeded for construction method selection of main work types using Artificial Intelligence like Fuzzy, AHP and Case-based reasoning. It is difficult to apply many different kinds of construction method selection to every main work type with consideration for characteristics of work types and condition of a construction site when selecting construction method in the field. Accordingly, this study proposed the decision-making model which can apply to fields easily. Using matrix analysis and liner transformation, this study verified consistency of study models applied in the process of soil retaining selection with a case study.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.23-30
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• 2018

With the development of ICT technology, the game industry is growing rapidly. However, due to the excessive play of the game, there are cases where normal life is difficult. It is necessary to study the diagnosis and countermeasures against such game addiction. This paper study a system for diagnosing game addiction based on game usage information of game users. In order to develop a game addiction diagnostic system, this study classifies game addiction and abuse users and collects and models log information in real game environment. In addition, the addiction and good use model of game users was used as a knowledge to diagnose the addiction of new game users. Through the experiment, we could confirm the meaningful results. Also, according to the result of the diagnosis, the service such as SMS and reporting was suggested.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.871-879
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• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• The Journal of Information Systems
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• v.31 no.1
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• pp.289-304
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• 2022

Purpose The development of the network and mobile industries has induced companies to invest in information systems, leading a new industrial revolution. The Journal of Information Systems, which developed the information system field into a theoretical and practical study in the 1990s, retains a 30-year history of information systems. This study aims to identify academic values and research trends of JIS by analyzing the trends. Design/methodology/approach This study aims to analyze the trend of JIS by compounding various methods, named as TLS mining analysis. TLS mining analysis consists of a series of analysis including Term Frequency-Inverse Document Frequency (TF-IDF) weight model, Latent Dirichlet Allocation (LDA) topic modeling, and a text mining with Semantic Network Analysis. Firstly, keywords are extracted from the research data using the TF-IDF weight model, and after that, topic modeling is performed using the Latent Dirichlet Allocation (LDA) algorithm to identify issue keywords. Findings The current study used the summery service of the published research paper provided by Korea Citation Index to analyze JIS. 714 papers that were published from 2002 to 2012 were divided into two periods: 2002-2011 and 2012-2021. In the first period (2002-2011), the research trend in the information system field had focused on E-business strategies as most of the companies adopted online business models. In the second period (2012-2021), data-based information technology and new industrial revolution technologies such as artificial intelligence, SNS, and mobile had been the main research issues in the information system field. In addition, keywords for improving the JIS citation index were presented.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• Journal of Internet of Things and Convergence
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• v.7 no.4
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• pp.23-28
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• 2021

Through the 4^th industrial revolution, it is rapidly developing in various fields such as artificial intelligence, AR/VR, and big data, and software is at the center. In the field of education as well, the importance of integrated education to support the development of technology is being emphasized, and in order to compete in software technology, securing human resources for software development should be prioritize in domestic. However, unlike the hardware-centric society of the past, the role of software technology human resources is very important, and the reality is that they are discharging human resources that are far from the human resources image that companies need. In this paper, present an immersed education model for training AR software professionals, and based on this, propose an evaluation index that can grasp the quality of the program of the immersed AR education model. Through the AR education model, it is expected that the weaknesses and strengths of the model can be identified, and it can contribute to setting the direction for improvement of the education program.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.