• Journal of Information Technology Applications and Management
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• 제29권3호
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.29-36
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• 2022

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.90-99
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• 2023

This paper deals with studying strategy about impact of deep learning (DL) on the factor of Korean economic growth. To study classification of impact factors of Korean economic growth, we suggest dynamic equation of microeconomy and study methods on economic growth impact of deep learning. Next step is to suggest DL model to dynamic equation with Korean economy data with growth related factors to classify what factor is import and dominant factors to build policy and education. DL gives an influence in many areas because it can be implemented with ease as just normal editing works and speak including code development by using huge data. Currently, young generations will take a big impact on their job selection because generative AI can do well as much as humans can do it everywhere. Therefore, policy and education methods should be rearranged as new paradigm. However, government and officers do not understand well how it is serious in policy and education. This paper provides method of policy and education for AI education including generative AI through analysing many papers and reports, and experience.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• 한국과학교육학회지
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• 제26권1호
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• pp.1-8
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• 2006

본 연구에서는 고등학교 학생들의 성취 목적(과제지향/수행 지향/수행 회피), 유능감 등의 동기 변인과 학습 전략(심층적/피상적), 자기 조절 능력 등의 인지 변인 및 화학 선다형 수리 문제 해결력 사이의 인과관계를 경로 분석을 통해 조사하였다. 연구 결과, 유능감과 과제 지향 목적은 자기 조절 능력을 통하여 화학 수리 문제 해결력에 긍정적 영향을 미쳤으며, 특히 유능감은 인지 변인을 경유하지 않고도 직접적으로 화학 수리 문제 해결력에 긍정적 영향을 주는 것으로 조사되었다. 인지 변인 중 심층적 학습 전략은 유능감과 과제 지향 목적의 영향을 받았고 피상적 학습 전략은 수행 회피 목적의 영향을 받았으나, 이러한 학습 전략과 화학 수리 문제 해결력 사이에는 인과관계가 존재하지 않았다.

• 전자통신동향분석
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• 제35권7호
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• pp.3-12
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• 2020

In this paper, we introduce ETRI AI Strategy #1, "Proactively Securing AI Core Technologies." The first goal of this strategy is to innovate artificial intelligence (AI) service technology to overcome the current limitations of AI technologies. Even though we saw a big jump in AI technology development recently due to the rise of deep learning (DL), DL still has technical limitations and problems. This paper introduces the four major parts of the advanced AI technologies that ETRI will secure to overcome the problems of DL and harmonize AI with the human world: post DL technology, human-AI collaboration technology, intelligence for autonomous things, and big data platform technology.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.803-812
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• 2022

With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio.

• Journal of Information Technology Applications and Management
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• 제17권3호
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• pp.57-69
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• 2010

Many organizations have had deep interests in studies concerning leadership and in academic areas, in not only management but also psychology. Until now, leadership has been accentuated by managers or team leaders especially. Recently, however, the concept of self-leadership directing one's own activities through self-control or self-management is being focused on practices and in academia. This study is to investigate the influence between self-leadership strategies and learning performance in IT classes mediated by attitude of attendance focused on the social science students in a university. Research results can give us direction of task-taking attitudes in firms or learning attitudes in teaching organizations and implications to human resource managers who are in charge of improving learning performance or productivity.

• 한국수소및신에너지학회논문집
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• 제33권5호
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• pp.591-597
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• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• 공학교육연구
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• 제17권6호
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• pp.20-29
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• 2014

The purpose of this study is to analyze the traits on the cognitive and affective learning style of university students. CALSIU(The Cognitive & Affective Learning Style Inventory for University School Students) by Kim, E. J. was modified for applying to university students and performed with 399 university students from three universities in Daejeon and Chungnam. Statistical analysis done in this study were ANOVA and Scheffe's test. Findings of the study are as follows : First, the students with high academic achievements have intuitive perception type, whole processing type, and deep storage & recall type. Secondly, the students with low academic achievement have strong non-academic learning type. Third, interaction attitude of affective learning styles is the important element to determine their academic achievement. The students with independent type get high academic achievements. Therefore, instructor should consider the learning styles of students, and it should be used to improve their teaching & learning strategy for better academic achievements of university students.