• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.161-167
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• 2019

In this paper, the performance verification of an around-view sonar and an excavation depth measuring sonar applicable to track-based ROVs (Remotely Operated underwater Vehicles) for heavy duty work is studied. For the performance verification, an experiment is carried out in a water tank and at sea by attaching the around-view sonar and the excavation depth measuring sonar for a heavy work ROV. In the case of the around-view sonar, image sonars are mounted on ROV in four directions (front, back, left and right) and in the case of the excavation depth measuring sonar, the same kind of MBES (Multi Beam Echo Sounder) is mounted on the front of the ROV. The result of an operation test of the ROV equipped with these sonars shows that the sonar systems are rarely affected by high turbidity due to sedimentation during the operation. In the case of the around-view sonar, it is possible to see rock formation, gravel and sandbank 30 m ahead of the ROV. It is confirmed that the excavation depth can be measured after the ROV has performed the excavation. This experiment demonstrates that the ROV can improve the efficiency of the work by utilizing the around-view sonar and the excavation depth measuring sonar.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.611-618
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• 2020

In this paper, we propose a design and implementation method of speed quiz content that can be driven by a social robot capable of interacting with humans, and a method of developing an intelligent module necessary for implementation. In addition, we propose a method of implementing speed quiz content through the process of constructing a map by arranging and connecting intelligent module blocks. Recently, software education has become mandatory and interest in programming is increasing. However, programming is difficult for students without basic knowledge of programming languages to directly access, and interest in block-type programming platforms suitable for beginners is growing. The block-type programming platform used in this paper is a platform that supports immediate and intuitive programming by supporting interactions between humans and robots. In this paper, the intelligent module implemented for the speed quiz content was used by blocking it within a block-type programming platform. In order to implement the scenario of the speed quiz content proposed in this paper, we implement a total of three image-based artificial intelligence modules. In addition to the intelligent module, various functional blocks were placed to implement the speed quiz content. In this paper, we propose a method of designing a speed quiz content scenario and a method of implementing an intelligent module for speed quiz content.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.63-70
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• 2022

Although 2D Object detection has been largely improved in the past years with the advance of deep learning methods and the use of large labeled image datasets, 3D object detection from 2D imagery is a challenging problem in a variety of applications such as robotics, due to the lack of data and diversity of appearances and shapes of objects within a category. Google has just announced the launch of Objectron that has a novel data pipeline using mobile augmented reality session data. However, it also is corresponding to 2D-driven 3D object detection technique. This study explores more mature 2D object detection method, and applies its 2D projection to Objectron 3D lifting system. Most object detection methods use bounding boxes to encode and represent the object shape and location. In this work, we explore a stochastic representation of object regions using Gaussian distributions. We also present a similarity measure for the Gaussian distributions based on the Hellinger Distance, which can be viewed as a stochastic Intersection-over-Union. Our experimental results show that the proposed Gaussian representations are closer to annotated segmentation masks in available datasets. Thus, less accuracy problem that is one of several limitations of Objectron can be relaxed.

• Applied Chemistry for Engineering
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• v.33 no.1
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• pp.83-89
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• 2022

Since the pandemic of COVID-19, active investigation to develop immunity to infectious disease by delivering nucleic acids has been proceeded. Particularly, many studies have been conducted on non-viral vector as several vital side-effects which were found on nucleic acid delivery system using viral vectors. In this study, we have developed plasmid DNA (pDNA) loaded-hyaluronic acid derivative (HA) coated-polyethyleneimine (PEI) based polyplex for enhanced nucleic acid delivery efficiency. We have optimized the ratio of pDNA : PEI : HA by measuring size and protein transcription efficiency. The final product, polyplex-HA, was characterized through measuring size, zeta-potential and TEM image. Intracellular uptake and protein transcription efficiency were compared to commercially available transfection reagent, lipofectamine, through fluorescence image and flow cytometry. In conclusion, polyplex-HA presents a novel gene delivery system for efficient and stable protein transcription since it is available for delivering various genetic materials and has less immunoreactivity.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.403-410
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• 2022

Non-Fungible Tokens (NFTs) created on the basis of blockchain have their own unique value, so they cannot be forged or exchanged with other tokens or coins. Using these characteristics, NFTs can be issued to digital assets such as images, videos, artworks, game characters, and items to claim ownership of digital assets among many users and objects in cyberspace, as well as proving the original. However, interest in NFTs exploded from the beginning of 2020, causing a lot of load on the blockchain network, and as a result, users are experiencing problems such as delays in computational processing or very large fees in the mining process. Additionally, all actions of users are stored in the blockchain, and digital assets are stored in a blockchain-based distributed file storage system, which may unnecessarily expose the personal information of users who do not want to identify themselves on the Internet. In this paper, we propose an NFT platform using cloud computing, access gate, conversion table, and cloud ID to improve usability and privacy problems that occur in existing system. For performance comparison between local and cloud systems, we measured the gas used for smart contract deployment and NFT-issued transaction. As a result, even though the cloud system used the same experimental environment and parameters, it saved about 3.75% of gas for smart contract deployment and about 4.6% for NFT-generated transaction, confirming that the cloud system can handle computations more efficiently than the local system.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.175-187
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• 2020

The purpose of this study is to discuss the elements and meanings of the performative scenography which has been revealed through the new directorial interpretation and deconstruction process of the musical 『All Shook Up』. The performative scenography characteristics after postmodernism aim to create individual active perceptions and various social meanings through audience's voluntary and particular emergence. To this end, the theoretical foundation of scenography was examined by periods in advance. Based on this, I attempted to establish performative scenography for synthesized scenic and media design through the reconstruction process for the 『All Shook Up Travelers』. As a result, I set up visual narrative based world of 『All Shook Up Travelers』 which was produced by text-based intense images for a direct medium in order to expand actors' inner narrative and established unique performative scenography of its own: 1. enhancing the adapted one's narratives for the actors' and audience's co-existence and detachment, 2. delivering its own independent meanings which have double meanings, 3. encouraging audience's critical and active perception experiences through collage and montage of media.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.185-207
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• 2022

Recently, with the development of deep learning technology, researches to apply a deep learning algorithm to analyze unstructured data such as text and images are being actively conducted. Text classification has been studied for a long time in academia and industry, and various attempts are being performed to utilize data characteristics to improve classification performance. In particular, a hierarchical relationship of labels has been utilized for hierarchical classification. However, the top-down approach mainly used for hierarchical classification has a limitation that misclassification at a higher level blocks the opportunity for correct classification at a lower level. Therefore, in this study, we propose a methodology for classifying hierarchical data using the autoencoder-based deeply supervised network that high-level classification does not block the low-level classification while considering the hierarchical relationship of labels. The proposed methodology adds a main classifier that predicts a low-level label to the autoencoder's latent variable and an auxiliary classifier that predicts a high-level label to the hidden layer of the autoencoder. As a result of experiments on 22,512 academic papers to evaluate the performance of the proposed methodology, it was confirmed that the proposed model showed superior classification accuracy and F1-score compared to the traditional supervised autoencoder and DNN model.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.