• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.25-29
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• 2021

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In this paper, focusing on the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.77-84
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• 2022

In this paper, we propose a novel unsupervised feature selection method. Conventional unsupervised feature selection method defines virtual label and uses a regression analysis that projects the given data to this label. However, since virtual labels are generated from data, they can be formed similarly in the space. Thus, in the conventional method, the features can be selected in only restricted space. To solve this problem, in this paper, features are selected using orthogonal projections and low-rank approximations. To solve this problem, in this paper, a virtual label is projected to orthogonal space and the given data set is also projected to this space. Through this process, effective features can be selected. In addition, projection matrix is restricted low-rank to allow more effective features to be selected in low-dimensional space. To achieve these objectives, a cost function is designed and an efficient optimization method is proposed. Experimental results for six data sets demonstrate that the proposed method outperforms existing conventional unsupervised feature selection methods in most cases.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.346-356
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• 2022

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.125-134
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• 2022

This paper implements an AR manual for a vehicle that can be used even in the vehicle interior space where it is difficult to apply the augmentation method of AR content, which is mainly used, and applies a deep learning model to improve the augmentation matching between real space and virtual objects. Through deep learning, the logo of the steering wheel is recognized regardless of the position, angle, and inclination, and 3D interior space coordinates are generated based on this, and the virtual button is precisely augmented on the actual vehicle parts. Based on the same learning model, the function to recognize the main warning light symbols of the vehicle is also implemented to increase the functionality and usability as an AR manual for vehicles.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1224-1232
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• 2022

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.96-101
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• 2023

In this work, an energy harvester (EH) on the rotating shaft has been proposed for a wireless sensor system (WSS) applications. The EH was designed and implemented to the shaft with a diameter of 20 cm to continuously power a wireless sensor system (WSS). The rotor has coils wound in pairs on seven U-shaped cores attached to the shaft. The stator consists of eight pairs of magnets attached to eight I-cores and they are fixed to an outer fixture. The generated power of the EH was investigated as function of the air gap between the rotor and stator, the number of turn of coils, and shaft speed. The fabricated EH produced power up to 2.87 W at 300 rpm and the 3 mm air gap.

• Smart Media Journal
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• v.12 no.7
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• pp.76-83
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• 2023

This study proposes a method to assist webtoon artists in the process of webtoon creation by utilizing a pretrained Text-to-Image model to generate webtoon images from text. The proposed approach involves fine-tuning a pretrained Stable Diffusion model using a webtoon dataset transformed into the desired webtoon style. The fine-tuning process, using LoRA technique, completes in a quick training time of approximately 4.5 hours with 30,000 steps. The generated images exhibit the representation of shapes and backgrounds based on the input text, resulting in the creation of webtoon-like images. Furthermore, the quantitative evaluation using the Inception score shows that the proposed method outperforms DCGAN-based Text-to-Image models. If webtoon artists adopt the proposed Text-to-Image model for webtoon creation, it is expected to significantly reduce the time required for the creative process.

• Advanced Industrial SCIence
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• v.2 no.1
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• pp.8-14
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• 2023

This study was performed as a preliminary experiment to develop functional feed additives using by-products generated during the production of black garlic. Therefore antioxidant and immune enhancing activity of black garlic pomace were measured. As a result of measuring the antioxidant activity of black garlic pomace, it was found antioxidant activity. Nitric oxide (NO) assay was performed to test the immune enhancing activity of vegetable samples including black garlic pomace among the samples used in the experiment. As a result of the NO assay experiment, highest concentrations of black garlic pomace, aster glehni, and MIX form produced NO, which Garlic pomace (69.4%), aster glehni (35.9%), and MIX (45.3%), respectively, compared to LPS (100%). In conclusion, it is considered that black garlic pomace contains an anti-inflammatory effect, and if the optimal mixing ratio of black garlic pomace and aster glehni is selected, it will be of sufficient value as a feed additive containing an anti-inflammatory effect.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.15-31
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• 2023

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.557-561
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• 2021

A meatadata has become an essential element in order to recommend video content to users. However, it is passively generated by video content providers. In the paper, a method for automatically generating metadata was studied in the existing manual metadata input method. In addition to the method of extracting emotion tags in the previous study, a study was conducted on a method for automatically generating metadata for genre and country of production through movie audio. The genre was extracted from the audio spectrogram using the ResNet34 artificial neural network model, a transfer learning model, and the language of the speaker in the movie was detected through speech recognition. Through this, it was possible to confirm the possibility of automatically generating metadata through artificial intelligence.