• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.327-335
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• 2021

This study verified the effectiveness of machine learning education programs focused on data labeling as an educational method for improving computational thinking of elementary school students. The education program was designed and developed based on the results of a preliminary demand analysis conducted on 100 elementary school teachers. In order to verify the effectiveness of the developed education program, 17 sixth-grade students attending K Elementary School were given 2 classes per day for a total of 6 weeks. In order to measure the effect of the training on improving computational thinking, the educational effects were analyzed by conducting pre-post-inspection using the "Beaver Challenge". According to the analysis, machine learning education focused on data labeling contributed to improving computational thinking of elementary school students.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.88-90
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• 2022

Semi Supervised Learning 은 일부의 data 에는 labeling 을 하고 나머지 data 에는 labeling 을 안한채로 학습을 진행하는 방법이다. Object Detection 은 이미지에서 여러개의 객체들의 대한 위치를 여러개의 바운딩 박스로 지정해서 찾는 Computer Vision task 이다. 당연하게도, model training 단계에서 사용되는 data set 의 크기가 크고 객체가 많을 수록 일반적으로 model 의 성능이 좋아 질 것이다. 하지만 실험 환경에 따라 data set 을 잘 확보하지 못하던가, 실험 장치가 데이터 셋을 감당하지 못하는 등의 문제가 발생 할 수 있다. 그렇기에 본 논문에서는 semi supervised learning based object detection model 을 알아보고 data set 의 크기를 조절해가며 modle 을 training 시킨 뒤 data set 의 크기에 따라 성능이 어떻게 변화하는 지를 알아 볼 것이다.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Korean Journal of Human Ecology
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• v.19 no.1
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• pp.167-177
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• 2010

This study examined consumer perception and use on labeling of cooking utensils made of plastic and glass to get information about improving the labeling. The data were collected from 505 adults in Seoul, Busan, Daegu, Daejeon, Incheon, and Gwangju. The data were analyzed by SPSS Windows V.14.0. Frequencies, t tests, one-way analysis of variance, and Duncan's multiple range tests were carried out. Many respondents checked off 'precautions in use' more than any other notice when they purchased the cooking utensils made of plastic and glass. Respondents were dissatisfied with the letter size and intelligibility of foreign language on the labeling. Most respondents preferred 'tag' for most cooking utensils made of plastic and glass. In addition, on necessity of precautions for each category of plastic cooking utensils, frying pans, plastic baskets, plastic water buckets, plastic seasoning bottles, the frying pan showed the highest need for 'do not place close to the fire'. Plastic cups and plastic containers showed the highest in 'whether utensils could be used in the microwave oven and accompanying precautions', and plastic cutting board showed the highest in 'matters relating to washing before use.' In the case of cooking utensils made of glass, 'precaution on shock' was the highest for glass cups and mugs and 'whether utensils could be used in the microwave oven and accompanying precautions' was the highest for glass pans, dishes and containers.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.181-196
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• 2016

The objective of this study is to evaluate the organization system and the labeling system of information architecture of a website for the elderly. To achieve this aims, we selected a representative website, i.e., Naver, and the participants were conducted given three types of search tasks using their own information literacy skills and they were answered to the questionnaire and an additional interview, if necessary. A total of 74 valid data were collected through the experiment, and we analyzed the data using SPSS Ver. 20. It revealed that Naver received a positive evaluation in the organization system aspect, particularly its systematic subject categorization and chronological browsing mechanisms. Old adults were preferred the icon-based labeling than the text-based labeling system, and showed a significant difference among their academic backgrounds.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.631-636
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• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1096-1107
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• 2019

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1065-1072
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• 2024

Previous approaches to 3D excavator pose estimation via synthetic data training utilized a single virtual excavator model, low polygon objects, relatively poor textures, and few background objects, which led to reduced accuracy when the resulting models were tested on differing excavator types and more complex backgrounds. To address these limitations, the authors present a realism-centric synthetization and labeling approach that synthesizes results with improved image quality, more detailed excavator models, additional excavator types, and complex background conditions. Additionally, the data generated includes dense pose labels and depth maps for the excavator models. Utilizing the realism-centric generation method, the authors achieved significantly greater image detail, excavator variety, and background complexity for potentially improved labeling accuracy. The dense pose labels, featuring fifty points instead of the conventional four to six, could allow inferences to be made from unclear excavator pose estimates. The synthesized depth maps could be utilized in a variety of DNN applications, including multi-modal data integration and object detection. Our next step involves training and testing DNN models that would quantify the degree of accuracy enhancement achieved by increased image quality, excavator diversity, and background complexity, helping lay the groundwork for broader application of synthetic models in construction robotics and automated project management.