• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.451-461
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• 2024

Recently, various AI chatbots based on large language models have been released. Chatbots have the advantage of providing users with quick and easy information through interactive prompts, making them useful in various fields such as question answering, writing, and programming. However, a vulnerability in chatbots called "prompt injection attacks" has been proposed. This attack involves injecting instructions into the chatbot to violate predefined guidelines. Such attacks can be critical as they may lead to the leakage of confidential information within large language models or trigger other malicious activities. However, the vulnerability of Korean prompts has not been adequately validated. Therefore, in this paper, we aim to generate malicious Korean prompts and perform attacks on the popular chatbot to analyze their feasibility. To achieve this, we propose a system that automatically generates malicious Korean prompts by analyzing existing prompt injection attacks. Specifically, we focus on generating malicious prompts that induce harmful expressions from large language models and validate their effectiveness in practice.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.967-971
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• 2023

Recently, a variety of AI-based platform services are available, and one of them is ChatGPT that processes a large quantity of data in the natural language and generates an answer after self-learning. ChatGPT can perform various tasks including software programming in the IT sector. Particularly, it may help generate a simple program and correct errors using C Language, which is a major programming language. Accordingly, it is expected that ChatGPT is capable of effectively using Verilog HDL, which is a hardware language created in C Language. Verilog HDL synthesis, however, is to generate imperative sentences in a logical circuit form and thus it needs to be verified whether the products are executed properly. In this paper, we aim to select small-scale logical circuits for ease of experimentation and to verify the results of circuits generated by ChatGPT and human-designed circuits. As to experimental environments, Xilinx ISE 14.7 was used for module modeling, and the xc3s1000 FPGA chip was used for module embodiment. Comparative analysis was performed on the use area and processing time of FPGA to compare the performance of ChatGPT products and Verilog HDL products.

• Journal of Internet Computing and Services
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• v.3 no.4
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• pp.83-94
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• 2002

Recently for software development productivity a lot of researches in the field of software engineering hove focuses on the component-based software product lines which allows the reuse of forger-granularity software components Its purpose is to develop the specific software application of quality more rapidly by instantiating and assembling the components populated in software product line assets The essential part to build the component-based software product lines is the quality of components, and one of the most important features determining the quality of components is 'reconfigurability' Component reconfigurability means the extent to which the reusers can change the functions and architecture of the component according to their context and environment. This paper proposes the component code generation technique which provides the reconfigurability at the time of code generation using The feature diagram and XML/XSLT technologies The approach of this paper allows the component reusers to get automatically their own component source code by providing only the values of variabilities represented in the feature diagram of the component family. The real world example, the code generation system for o list container family, shows the applicability of the feature model and XML related technologies in the area of the generative programming. Our approach should be basis to build the component based software product lines and extensible to support the larger graularity components.

• The Mathematical Education
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• v.63 no.3
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• pp.467-484
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• 2024

The rapid advancement of generative AI has ushered in an era where anyone can create and freely utilize personalized chatbots without the need for programming expertise. This study aimed to develop a customized chatbot based on OpenAI's GPTs for the purpose of pre-service teacher education and to analyze its educational performance in mathematics as assessed by educators guiding pre-service teachers. Responses to identical questions from a general-purpose chatbot (ChatGPT), a customized GPTs-based chatbot, and an elementary mathematics education expert were compared. The expert's responses received an average score of 4.52, while the customized GPTs-based chatbot received an average score of 3.73, indicating that the latter's performance did not reach the expert level. However, the customized GPTs-based chatbot's score, which was close to "adequate" on a 5-point scale, suggests its potential educational utility. On the other hand, the general-purpose chatbot, ChatGPT, received a lower average score of 2.86, with feedback indicating that its responses were not systematic and remained at a general level, making it less suitable for use in mathematics education. Despite the proven educational effectiveness of conventional customized chatbots, the time and cost associated with their development have been significant barriers. However, with the advent of GPTs services, anyone can now easily create chatbots tailored to both educators and learners, with responses that achieve a certain level of mathematics educational validity, thereby offering effective utilization across various aspects of mathematics education.