• International conference on construction engineering and project management
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• 2022.06a
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.93-98
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• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.71-93
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• 2001

Commonly to design a database system. a conceptual database has to be designed and then it is transformed into a logical database schema prior to building a target database system. This paper proposes a method which transforms a Temporal Entity-Relationship Model(TERM) into a Temporal Object-Oriented Model(TOOM) to build an efficient database schema. I formalize the time concept in view of object versioning and specify the constraints required during transformation procedure. The proposed transformation method contributes to getting the logical temporal data from the conceptual temporal events Without any loss of semantics, Compared to other approaches of supporting various properties, this approach is more general and efficient because it is the semantically seamless transformation method by using the orthogonality of types of objects, semantics of relationships and constraints over roles.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.189-196
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• 2016

The purpose of this study is to investigate the causal relationships among self-efficacy, perceived usefulness, supervisor and peer support, satisfaction, and transfer intentions in the corporate mobile learning. For this study, the web survey was administered to 302 mobile learning learners of the A domestic corporation in South Korea. Structural equation modeling(SEM) analysis was conducted in order to examine the causal relationships among the variables. The results indicated that first, self-efficacy, perceived usefulness, and supervisor and peer support had positive effects on satisfaction. Second, supervisor and peer support and satisfaction had positive effects on transfer intentions. Third, satisfaction mediated the relationship between self-efficacy and perceived usefulness, while it did partially the relationship between supervisor and peer support and transfer intentions. Based on the result of the research, the study proposes organizational environment with cooperative supervisor and peer support should be made in order to improve the level of learners' transfer intentions. In addition, learning strategies that facilitate learners' self-efficacy and mobile information technology acceptance are needed to develop for enhancing the learners' satisfaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6303-6309
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• 2014

In this thesis, PBL was applied to the subject for improving students' many skills that modern industrial society demands. Our engineering school developed PBL problems for PBL use, applied the problems to classes and confirmed the effectiveness of PBL. The study subjects were 63 freshman students in H University who took the 'Introduce of computer engineering'. We applied 5 PBL problems for 15 weeks. They wrote and submitted a reflective journal when they finished the every given PBL activity. In addition, they completed a class evaluation form after the activity of 5th PBL Problem ended. The study showed that the students experienced the effectiveness of PBL, such as the comprehension of the studied contents, the comprehension of the cooperative learning, authentic experience, creative problem-solving skills, presentation skills, communication ability, self-directed study ability and confidence. Some difficulties in gathering together and spending much time were also encountered. The students realized that the PBL learning activities were important methods because the students could develop into future intelligent engineers that modern industrial society demands through PBL learning activities. The main goal of an engineering school is to produce specialists with creative problem solving ability so that the effects of this study are quite promising for our engineering school.

• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.163-171
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• 2003

There are making epoch changes of a teaching method and educational contents as information communication technology (ICT) introduced to school education. The 7th curriculum induces that teaching hours should be assigned to education using ICT more than 10% of all classroom work hours. In this study, we established basic course of courseware design for applying ICT to physical education effectively in the 7th curriculum. We developed distance-learning program for motion of short-distance race and the entire his power race in middle school using Web. Flash and Database. And we evaluated efficiency by students that are 4 classrooms (140 persons) after teaching using the WBI. According to results of questions and a test, the students expressed that education using ICT make understanding of instruction substance effectively. concentrating their attention on teaching and concerning in teaching actively.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.991-998
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• 2004

In many practical teaming problems including bioinformatics area, there is a small amount of labeled data along with a large pool of unlabeled data. Labeled examples are fairly expensive to obtain because they require human efforts. In contrast, unlabeled examples can be inexpensively gathered without an expert. A common method with unlabeled data for data classification and analysis is co-training. This method uses a small set of labeled examples to learn a classifier in two views. Then each classifier is applied to all unlabeled examples, and co-training detects the examples on which each classifier makes the most confident predictions. After some iterations, new classifiers are learned in training data and the number of labeled examples is increased. In this paper, we propose a new co-training strategy using unlabeled data. And we evaluate our method with two classifiers and two experimental data: WebKB and BIND XML data. Our experimentation shows that the proposed co-training technique effectively improves the classification accuracy when the number of labeled examples are very small.

• Journal of The Korean Association of Information Education
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• v.13 no.3
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• pp.273-280
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• 2009

The internet becomes a new communication tool in the knowledge and information society and the people are expanded at the place of information interchange and exchange of view. In recent robot education institutions provide their own official homepages to introduce the robot educational resources. But because they have restrictive searching the functions and providing general robot education resources and don't offer a place that teachers can express their thoughts and share common interests with other users, online community among teachers for robot education and users couldn't have built. In this paper, we propose an Online Robot Education Community(OREC) that teachers and users in different robot education institutions can interchange or share their technical information, learn robot techniques, participate in discussion of their experiences on work, share their common interests, and be provided updated latest news in real-time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.160-161
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• 2017

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language Opinion mining and sentiment analysis(OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2007-2016. For the literature analysis, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, research areas. More detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexcon-based) used in these publications, levels (documents, sentences or aspect-level) of sentiment analysis work done and major application areass of OMSA.