• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.1
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• pp.45-52
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• 2015

Although researchers have proposed various recommendation systems, most recommendation approaches are for single users and there are only a small number of recommendation approaches for groups. However, TV programs or movies are most often viewed by groups rather than by single users. Most recommendation approaches for groups assume that single users' profiles are known and that group profiles consist of the single users' profiles. However, because it is difficult to obtain group profiles, researchers have only used synthetic or limited datasets. In this paper, we report on various group recommendation approaches to a real large-scale dataset in a TV domain, and evaluate the various group recommendation approaches. In addition, we provide some guidelines for group recommendation systems, focusing on home group users in a TV domain.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.893-903
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• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.

• Bulletin of the Korean Chemical Society
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• v.29 no.10
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• pp.1998-2004
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• 2008

Two synthetic pathways to substituted hexahydroimidazo[1,2-a]pyridines, which may serve as precursors of aza-alkaloids, were investigated. The first involves the condensation of a bisnucleophilic enediaminoester and a biselectrophile. The second involves attachment to nitrogen of the carbon chain skeleton required to form the six-memberd ring, before formation of the enediaminoester. Several substituted hexahydroimidazo[1,2- a]pyridines were synthesized via these two approaches.

• Proceedings of the PSK Conference
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• 2001.04a
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• pp.66-69
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• 2001

• Proceedings of the PSK Conference
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• 2001.10a
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• pp.230.3-231
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• 2001

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.37-42
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• 2019

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

• The Journal of the Acoustical Society of Korea
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• v.40 no.6
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• pp.546-554
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• 2021

In the signal processing of synthetic aperture sonar, it is subject that the platform in which the sensor array is installed moves along the straight line path. In practical operation in underwater, however, the sensor platform will have trajectory disturbances, diverting from the line path. It causes phase errors in measured signals and then produces deteriorated SAS images. In this study, in order to develop towed SAS, as tools to remove the phase errors associated with the trajectory disturbances of the towfish, motion compensation technique using Redundant Phase Center (RPC) and also Phase Gradient Autofocus (PGA) method is investigated. The performances of these two approaches are examined by means of a simulation for SAS system having a sway disturbance.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1308-1308
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• 2024

Computer vision techniques have been widely employed in automated construction management to enhance safety and prevent accidents at construction sites. However, previous research in the field of vision-based approaches has often overlooked small-sized construction tools. These tools present unique challenges in data collection due to their diverse shapes and sizes, as well as in improving model performance to accurately detect and classify them. To address these challenges, this study aimed to enhance the performance of vision-based classifiers for small-sized construction tools, including bucket, cord reel, hammer, and tacker, by leveraging synthetic images generated from a 3D virtual environment. Three classifiers were developed using the YOLOv8 algorithm, each differing in the composition of the training dataset: (i) 'Real-4000', trained on 4,000 authentic images collected through web crawling methods (1,000 images per object); (ii) 'Hybrid-4000', consisting of 2,000 authentic images and 2,000 synthetic images; and (iii) 'Hybrid-8000', incorporating 4,000 authentic images and 4,000 synthetic images. To validate the performance of the classifiers, 144 directly-captured images for each object were collected from real construction sites as the test dataset. The mean Average Precision at an IoU threshold of 0.5 (mAP_0.5) for the classifiers was 79.6%, 90.8%, and 94.8%, respectively, with the 'Hybrid-8000' model demonstrating the highest performance. Notably, for objects with significant shape variations, the use of synthetic images led to the enhanced performance of the vision-based classifiers. Moreover, the practical applicability of the proposed classifiers was validated through confidence scores, particularly between the 'Hybrid-4000' and 'Hybrid-8000' models. Statistical analysis using t-tests indicated that the performance of the 'Hybrid-4000' model would either matched or exceeded that of the 'Hybrid-8000'model based on confidence scores. Thus, employing the 'Hybrid-4000' model may be preferable in terms of data collection efficiency and processing time, contributing to enhanced safety and real-time automation and robotics in construction practices.

• Genomics & Informatics
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• v.8 no.4
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• pp.177-184
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• 2010

The target of rapamycin (TOR) signaling pathway is a conserved pathway that regulates eukaryotic cell growth in response to environmental cues. Chemical genomic approaches that profile rapamycin sensitivity of yeast deletion strains have given insights into the function of TOR signaling pathway. In the present study, we analyzed the rapamycin sensitivity of yeast deletion library strains on synthetic medium. As a result, we identified 130 strains that are hypersensitive or resistant to rapamycin compared with wild-type cells. Among them, 36 genes are newly identified to be related to rapamycin sensitivity. Moreover, we found 16 strains that show alteration in rapamycin sensitivity between complex and synthetic media. We suggest that these genes may be involved in part of TOR signaling activities that is differentially regulated by media composition.

• Journal of Hydrogen and New Energy
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• v.24 no.3
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• pp.193-199
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• 2013

In this work, thin film type Cu/ZnO nanostructure catalysts were fabricated by several synthetic routes in order to maximize the performance of the micro reforming system. For this work, various Cu/ZnO nanostructure catalysts could be synthesized by means of four approaches which are chemical vapor method, wet solution method and their hybrid method. The reforming performance of these as-synthetic catalysts was evaluated as compared to the conventional catalysts. Among the as-synthetic nanostructures, sphere type catalysts with specific surface of $18.6m^2/g$ showed the best performance of hydrogen production rate of 30ml/min at the feed rate of 0.2ml/min. This work will give the first insight on thin film type Cu/ZnO nanostructure catalyst for micro reforming system for hydrogen production of portable electronic systems.