• Smart Media Journal
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• v.11 no.3
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• pp.26-30
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• 2022

Spiking neural network is a neural network that applies the working principle of real brain neurons. Due to the biological mechanism of neurons, it consumes less power for training and reasoning than conventional neural networks. Recently, as deep learning models become huge and operating costs increase exponentially, the spiking neural network is attracting attention as a third-generation neural network that connects convolution neural networks and recurrent neural networks, and related research is being actively conducted. However, in order to apply the spiking neural network model to the industry, a lot of research still needs to be done, and the problem of model retraining to apply a new model must also be solved. In this paper, we propose a method to minimize the cost of model retraining by extracting the weights of the existing trained deep learning model and converting them into the weights of the spiking neural network model. In addition, it was found that weight conversion worked correctly by comparing the results of inference using the converted weights with the results of the existing model.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.529-534
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• 2021

Computing thinking is regarded as one of the important skills required in the 21st century, and many countries have introduced and implemented computing thinking training courses. Among computational thinking education methods, educational game-based methods increase student participation and motivation, and increase access to computational thinking. Autothinking is an educational game developed for the purpose of providing computational thinking education to learners. It is an adaptive system that dynamically provides feedback to learners and automatically adjusts the difficulty according to the learner's computational thinking ability. However, because the game was designed based on rules, it cannot intelligently consider the computational thinking of learners or give feedback. In this study, game data collected through Autothikning is introduced, and game score prediction that reflects computational thinking is performed in order to increase the adaptability of the game by using it. To solve this problem, a comparative study was conducted on linear regression, decision tree, random forest, and support vector machine algorithms, which are most commonly used in regression problems. As a result of the study, the linear regression method showed the best performance in predicting game scores.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.480-502
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• 2022

This work assesses the degree of satisfaction tourists receive as final recipients in a tourism destination based on the fact that satisfied tourists can make a significant contribution to the growth and continuous improvement of a tourism business. The work considers Pokhara, the tourism capital of Nepal as a prefecture of study. A stratified sampling methodology with open-ended survey questions is used as a primary source of data for a sample size of 1019 for both international and domestic tourists. The data collected through a survey is processed using a data mining tool to perform multi-dimensional analysis to discover information patterns and visualize clusters. Further, supervised machine learning algorithms, kNN, Decision tree, Support vector machine, Random forest, Neural network, Naive Bayes, and Gradient boost are used to develop models for training and prediction purposes for the survey data. To find the best model for prediction purposes, different performance matrices are used to evaluate a model for performance, accuracy, and robustness. The best model is used in constructing a learning-enabled model for predicting tourists as satisfied, neutral, and unsatisfied visitors. This work is very important for tourism business personnel, government agencies, and tourism stakeholders to find information on tourist satisfaction and factors that influence it. Though this work was carried out for Pokhara city of Nepal, the study is equally relevant to any other tourism destination of similar nature.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.44-57
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• 2022

Fablab is a global network of digital fabrication facilities. Fablab is a digital workshop for individual manufacturing. In addition, Fablab is a next-generation digital infrastructure that connects education, training, R&D and production. The Fablab is a facility lab that makes the products you want, and it is a space where users-led products or services are discovered for the community. Furthermore, through various citizen-led projects, it is playing the role of innovation that changes the region and society. In this study, we examine the operating conditions of Fablabs in Korea and Japan (Fablab Seoul, Fablab Busan, Fablab Kamakura and Fablab Kitakagaya). It also explores the business model and sustainable development potential of each fablab. To this end, first, we compare and analyze the use of fablabs in both countries. Second, the purpose of the fablabs of both countries is analyzed. Third, we analyze the business models that the fablabs of both countries are taking for sustainable development through Lean Canvas. Based on the results obtained through case analysis of both countries, we make suggestions for the development of fablabs in Korea.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.13-18
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• 2022

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.