• International conference on construction engineering and project management
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• 2024.07a
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• pp.1286-1288
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• 2024

The construction industry, known for its inherent risks and multiple hazards, necessitates effective solutions for hazard identification and mitigation [1]. To address this need, the implementation of machine learning models specializing in object detection has become increasingly important because this technological approach plays a crucial role in augmenting worker safety by proactively recognizing potential dangers on construction sites [2], [3]. However, the challenge in training these models lies in obtaining accurately labeled datasets, as conventional methods require labor-intensive labeling or costly measurements [4]. To circumvent these challenges, synthetic data generation (SDG) has emerged as a key method for creating realistic and diverse training scenarios [5], [6]. The paper reviews the evolution of synthetic data generation tools, highlighting the shift from earlier solutions like Synthpop and Data Synthesizer to advanced game engines[7]. Among the various gaming platforms, Unity 3D and Unreal Engine stand out due to their advanced capabilities in replicating realistic construction hazard environments [8], [9]. Comparing Unity 3D and Unreal Engine is crucial for evaluating their effectiveness in SDG, aiding developers in selecting the appropriate platform for their needs. For this purpose, this paper conducts a comparative analysis of both engines assessing their ability to create high-fidelity interactive environments. To thoroughly evaluate the suitability of these engines for generating synthetic data in construction site simulations, the focus relies on graphical realism, developer-friendliness, and user interaction capabilities. This evaluation considers these key aspects as they are essential for replicating realistic construction sites, ensuring both high visual fidelity and ease of use for developers. Firstly, graphical realism is crucial for training ML models to recognize the nuanced nature of construction environments. In this aspect, Unreal Engine stands out with its superior graphics quality compared to Unity 3D which typically considered to have less graphical prowess [10]. Secondly, developer-friendliness is vital for those generating synthetic data. Research indicates that Unity 3D is praised for its user-friendly interface and the use of C# scripting, which is widely used in educational settings, making it a popular choice for those new to game development or synthetic data generation. Whereas Unreal Engine, while offering powerful capabilities in terms of realistic graphics, is often viewed as more complex due to its use of C++ scripting and the blueprint system. While the blueprint system is a visual scripting tool that does not require traditional coding, it can be intricate and may present a steeper learning curve, especially for those without prior experience in game development [11]. Lastly, regarding user interaction capabilities, Unity 3D is known for its intuitive interface and versatility, particularly in VR/AR development for various skill levels. In contrast, Unreal Engine, with its advanced graphics and blueprint scripting, is better suited for creating high-end, immersive experiences [12]. Based on current insights, this comparative analysis underscores the user-friendly interface and adaptability of Unity 3D, featuring a built-in perception package that facilitates automatic labeling for SDG [13]. This functionality enhances accessibility and simplifies the SDG process for users. Conversely, Unreal Engine is distinguished by its advanced graphics and realistic rendering capabilities. It offers plugins like EasySynth (which does not provide automatic labeling) and NDDS for SDG [14], [15]. The development complexity associated with Unreal Engine presents challenges for novice users, whereas the more approachable platform of Unity 3D is advantageous for beginners. This research provides an in-depth review of the latest advancements in SDG, shedding light on potential future research and development directions. The study concludes that the integration of such game engines in ML model training markedly enhances hazard recognition and decision-making skills among construction professionals, thereby significantly advancing data acquisition for machine learning in construction safety monitoring.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.7-14
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• 2021

Purpose: To solve urban problems, empirical research is being actively conducted to implement a smart city based on various ICT technologies, and digital twin technology is needed to effectively implement a smart city. A digital twin is essential for the realization of a smart city. A digital twin is a virtual environment that intuitively visualizes multidimensional data in the real world based on 3D. Digital twin is implemented on the premise of the convergence of GIS and BIM, and in particular, a lot of time is invested in data pre-processing and labeling in the data construction process. In digital twin, data quality is prioritized for consistency with reality, but there is a limit to data inspection with the naked eye. Therefore, in order to improve the required time and quality of digital twin construction, it was attempted to detect a building using Mask R-CNN, a deep learning-based masking algorithm for aerial images. If the results of this study are advanced and used to build digital twin data, it is thought that a high-quality smart city can be realized.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.55-60
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• 2018

기계학습 기반인 관계추출 모델을 설계할 때 다량의 학습데이터를 빠르게 얻기 위해 원격지도학습 방식으로 데이터를 수집한다. 이러한 데이터는 잘못 분류되어 학습데이터로 사용되기 때문에 모델의 성능에 부정적인 영향을 끼칠 수 있다. 본 논문에서는 이러한 문제를 강화학습 접근법을 사용해 해결하고자 한다. 본 논문에서 제안하는 모델은 오 분류된 데이터로부터 좋은 품질의 데이터를 찾는 문장선택기와 선택된 문장들을 가지고 학습이 되어 관계를 추출하는 관계추출기로 구성된다. 문장선택기는 지도학습데이터 없이 관계추출기로부터 피드백을 받아 학습이 진행된다. 이러한 방식은 기존의 관계추출 모델보다 좋은 성능을 보여주었고 결과적으로 원격지도학습데이터의 단점을 해결한 방법임을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2359-2376
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• 2022

With the exponential growth of medical image big data represented by high-resolution CT images(CTI), the high-resolution CTI data is of great importance for clinical research and diagnosis. The paper takes lung CTI as an example to study. Retrieving answer CTIs similar to the input one from the large-scale lung CTI database can effectively assist physicians to diagnose. Compared with the conventional content-based image retrieval(CBIR) methods, the CBIR for lung CTIs demands higher retrieval accuracy in both the contour shape and the internal details of the organ. In traditional supervised deep learning networks, the learning of the network relies on the labeling of CTIs which is a very time-consuming task. To address this issue, the paper proposes a Weakly Supervised Similarity Evaluation Network (WSSENet) for efficiently support similarity analysis of lung CTIs. We conducted extensive experiments to verify the effectiveness of the WSSENet based on which the CBIR is performed.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.124-131
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• 2024

Breast cancer classification using ultrasound, while widely employed, faces challenges due to its relatively low predictive value arising from significant overlap in characteristics between benign and malignant lesions, as well as operator-dependency. To alleviate these challenges and reduce dependency on radiologist interpretation, the implementation of automatic breast cancer classification in ultrasound image can be helpful. To deal with this problem, we propose a semi-supervised deep learning framework for breast cancer classification. In the proposed method, we could achieve reasonable performance utilizing less than 50% of the training data for supervised learning in comparison to when we utilized a 100% labeled dataset for training. Though it requires more modification, this methodology may be able to alleviate the time-consuming annotation burden on radiologists by reducing the number of annotation, contributing to a more efficient and effective breast cancer detection process in ultrasound images.

• Journal of Korean Home Economics Education Association
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• v.26 no.4
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• pp.1-19
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• 2014

The purpose of this study was to investigate perception of snack purchasing behaviors and labeling of foods and nutrition of the middle school students. The survey was conducted from 424 students who are boys and girls of middle school in Naju. Data were analyzed by a SPSS program. According to the survey, snack purchase place had a significant difference between gender(p<.05), and all of the boys and girls responded at a high rate that it's convenience store. Snack eating frequency had a significant difference between boys and girls(p<.05). Boys responded that they ate 1~2 times per a week the most, and girls responded that they ate 1~2 times per a month the most. As for snack purchasing behaviors, depending on gender, that of boys was 2.76 in average and that of girls was 2.87, lower than middle. The reason why students didn't check up food labeling, depending on gender and all of the boys and girls responded 'Expiration date' was first confirmed. As for the understanding of food expression, depending on snack expenses, the reason why food expression contents were hard showed a significant difference, depending on snack expenses(p<.05). As for the understanding of food expression, depending on snack purchase attitude, the students showed a significant difference, depending on snack purchase attitude(p<.001), and the lower the snack purchase attitude was, the less the students checked up snack expression. The reason why students checked up nutrition labeling a significant difference, depending on gender, snack expense and snack purchase attitude(p<.001). 'Weight management' was the highest. The recognition in the necessity of nutrition expression(p<.001) and the necessity of food/ nutrition education and publicity(p<.01) showed significant difference.

• Journal of Korean Home Economics Education Association
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• v.23 no.1
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• pp.113-141
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• 2011

The purpose of this study was to examine the effects of home economics instruction in food labeling using a motivational(ARCS-Attention, Relevance, Confidence, and Satisfaction) strategy to increase middle school students' learning motivation, recognition and use of food labels. To achieve this purpose, teaching-learning plans of food label instruction using a motivation(ARCS) strategy were developed over four class periods using a pretest-posttest experimental design. The experiment was conducted across two groups as follows: 4 experimental groups that received the motivation(ARCS) strategy instruction, and 3 comparative groups that received lecture type instruction. The pretest-posttest scores of the experimental and comparative groups were compared. The 203 data of questionnaires for the experiment were analyzed and evaluated by Analysis of Covariance(ANCOVA) using SPSS Win 12,0. The results of this study were as follows: First, teaching-learning plans, learning materials, and teacher reference materials for the home economics food label instruction that applied the motivation(ARCS) strategy were developed in five subject areas: nutrition labels, food additives, genetically modified food, irradiated food, and food quality verification labels. Second, students' learning motivation of the two groups showed statistically meaningful differences. Home economics instruction using a motivation(ARCS) strategy was more effective in increasing students' learning motivation than lecture type instruction. Third, as a result of ANCOVA which regulated the recognition of food labels in the pre-experimental design, the recognition of food labels in the post-experimental design showed the meaningful differences depending on the instruction style(motivation strategy and lecture type instruction). In addition, comprehensibility, practical use and educational necessity of food label details showed statistically meaningful differences. Home economics instruction using motivation(ARCS) strategy was more effective than lecture type instruction in improving students' recognition of food labeling. Fourth, as a result of ANCOVA which regulated the use of food labels in the pre-experimental stage, the use of food labels in the post-experimental stage showed meaningful differences between experimental and comparative groups depending on the instruction style. Therefore, home economics instruction in food labeling using motivation(ARCS) strategy was more effective than lecture type instruction in increasing students' use of food labels.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.679-681
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• 2003

The objectives of this research are as follows. First, to investigate methods for a national-scale land cover map based on multi-temporal classification of MODIS data and multi-spectral classification of Landsat TM data. Second, to investigate methods to p roduce ecological zone maps of Korea based on vegetation, climate, and topographic characteristics. The results of this research can be summarized as follows. First, NDVI and EVI of MODIS can be used to ecological mapping of the country by using monthly phenological characteris tics. Second, it was found that EVI is better than NDVI in terms of atmospheric correction and vegetation mapping of dense forests of the country. Third, several ecological zones of the country can be identified from the VI maps, but exact labeling requires much field works, and sufficient field data and macro-environmental data of the country. Finally, relationship between land cover types and natural environmental factors such as temperature, precipitation, elevation, and slope could be identified.

• Journal of Internet Computing and Services
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• v.18 no.2
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• pp.61-73
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• 2017

In this paper, we propose a Documents Cluster Labeling method using information content of words in clusters to understand what the clusters imply. To do so, we calculate the weight and frequency of the words. These two measures are used to determine the weight among the words in the cluster. As a nest step, we identify the candidate labels using the WordNet. At this time, the candidate labels are matched to least common hypernym of the words in the cluster. Finally, the representative labels are determined with respect to information content of the words and the weight of the words. To prove the superiority of our method, we perform the heuristic experiment using two kinds of measures, named the suitability of the candidate label ($Suitability_{cl}$) and the appropriacy of representative label ($Appropriacy_{rl}$). In applying the method proposed in this research, in case of suitability of the candidate label, it decreases slightly compared with existing methods, but the computational cost is about 20% of the conventional methods. And we confirmed that appropriacy of the representative label is better results than the existing methods. As a result, it is expected to help data analysts to interpret the document cluster easier.

• The Korean Journal of Nuclear Medicine
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• v.29 no.1
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• pp.98-104
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• 1995

Recently murine monoclonal antibodies have been studied actively for radioimmuno-scintigraphy and radioimmunotherapy, especially on patients with leukemia and lymphoma. In this research, we studied radiolabeling and immunologic characteristics of two in-house anti-leukemic monoclonal antibodies(anti-CALLA & anti-JL-1 antibodies) to make the basis for their clinical application. Each antibody was radiolabeled successfully with $^{99m}Tc$ by pretargeting transchelation method and with $^{125}I$ by lodogen method. We also studied cell binding assay, Scatchard analysis and modulation phenomenon. $^{125}I$ showed 90% labeling efficiency for each anti-body which was satisfactory, but $^{99m}Tc$ showed labeling efficiency below 70%, for which we need better labeling method. In cell binding assay, the immunoreactivity(IR) was low for $^{99m}Tc$-labeled antibodies. Scatchard analysis showed satisfactory data for both binding affinity. The affinity constant and antibody binding sites per cell are around $10^9M^{-1}$ and $10^4$, respectively. There was no modulation phenomenon in cases of $^{125}I$ or $^{99m}Tc$ labeled antibodies. We expect that two anti-leukemic monoclonal antibodies may be useful in diagnosis and therapy for leukemia and lymphoma patients.