• Journal of the Institute of Convergence Signal Processing
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• v.24 no.3
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• pp.160-165
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• 2023

In this study, we propose an SNS sentence writing assistance system that utilizes YOLO and GPT to assist users in writing texts with images, such as SNS. We utilize the YOLO model to extract objects from images inserted during writing, and also extract meta-information such as GPS information and creation time information, and use them as prompt values for GPT. To use the YOLO model, we trained it on form image data, and the mAP score of the model is about 0.25 on average. GPT was trained on 1,000 blog text data with the topic of 'restaurant reviews', and the model trained in this study was used to generate sentences with two types of keywords extracted from the images. A survey was conducted to evaluate the practicality of the generated sentences, and a closed-ended survey was conducted to clearly analyze the survey results. There were three evaluation items for the questionnaire by providing the inserted image and keyword sentences. The results showed that the keywords in the images generated meaningful sentences. Through this study, we found that the accuracy of image-based sentence generation depends on the relationship between image keywords and GPT learning contents.

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.15-30
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• 2023

Recently, various services using artificial intelligence(AI) are emerging in the media field as well However, most of the video editing, which involves finding an editing point and attaching the video, is carried out in a passive manner, requiring a lot of time and human resources. Therefore, this study proposes a methodology that can detect the edit points of video according to whether person in video are spoken by using Video Swin Transformer. First, facial keypoints are detected through face alignment. To this end, the proposed structure first detects facial keypoints through face alignment. Through this process, the temporal and spatial changes of the face are reflected from the input video data. And, through the Video Swin Transformer-based model proposed in this study, the behavior of the person in the video is classified. Specifically, after combining the feature map generated through Video Swin Transformer from video data and the facial keypoints detected through Face Alignment, utterance is classified through convolution layers. In conclusion, the performance of the image editing point detection model using facial keypoints proposed in this paper improved from 87.46% to 89.17% compared to the model without facial keypoints.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.47-65
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• 2023

As stakeholder demands and sustainable finance grow, ESG management and ESG evaluation are becoming important. SMEs should also prepare for the trends of ESG rating practices that affects supply chain management and financial transactions. However, SMEs have no choice but to focus on survival first, so there are restrictions on putting into ESG management. In addition, there is a lack of research on the legitimacy of ESG management by SMEs, and volatility in ESG evaluation systems and rating grades is also increasing. Accordingly, it is necessary to review ESG evaluation trends and practical guidelines along with the review of previous studies. As a result of the exploratory study, SMEs need to implement ESG management and make efforts to specialize in ESG related new businesses under conditions in which their survival base is guaranteed in terms of implementation strategies. In addition, it is necessary to focus on the strategic use of various evaluation results along with accumulating information favorable for ESG evaluation through organizational learning and software management. The implications of this study are that various studies such as the classification criteria for SMEs and the relationship between ESG evaluation grades and long-term survival rates are needed in ESG evaluation of SMEs. At the government policy level, it is time to consider the ESG evaluation system exclusively for SMEs so that ESG management can be implemented and ESG evaluation at different levels by industry and size.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of The Korean Association For Science Education
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• v.27 no.4
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• pp.318-327
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• 2007

The purpose of this study is to examine the effects of small-scale chemistry(SSC) laboratory activities implemented in high school chemistry II classes on the students' inquiry process skills and science-related attitudes. For this study, 112 students in the 12th grade were chosen and divided into an experimental and a control group. Seven SSC lab programs that can replace the traditional experiments in chemistry II textbooks were selected and administered to the experimental group while the traditional textbook experiments were administered to the control group. The results showed that there was a significant difference in the enhancement of inquiry process skills between the two groups while no significant difference was found in science-related attitudes. Further analysis showed that the difference in the inquiry process skills came from the basic inquiry process skills. The experimental group students thought that the SSC experiments have many advantages compared to the traditional experiments, e.g., individual work, learning lab and theory in parallel, short experiment time, safety, environmental aspects, etc. These results suggest that the SSC lab programs are valuable in high school chemistry classes and developing and distributing various SSC lab programs is needed to replace the traditional experiments in the current textbooks.

• Journal of The Korean Association For Science Education
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• v.29 no.6
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• pp.712-729
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• 2009

This study aims to research high school students' misconception of botanic photosynthesis and respiration, and as the measure of rectifying the misconception, to develop the teaching program based on Driver's conceptual change model, applying it to classes and observing the effect. Selected as the research subject was sixty-six students in 1st year of a highschool located in Busan who had chosen Biology Learning as discretionary subject, with their conceptual level on botanic photosynthesis and respiration researched through tests in drawing and descriptive writing. As a consequence of applying drawing as a way of classifying the levels of students' misconception on photosynthesis and respiration, many students' drawings included their misconception caused by textbooks or scientists, but after application of Driver's conceptual change model, they drew scientific drawings including the fundamental factors of botanic photosynthesis and respiration such as light, carbon dioxide, water, glucose, oxygen, leaf, chloroplast, mitochondria, stoma, and energy. Likewise, as a result of the descriptive writing test implemented for researching the students' conception on the various aspects of botanic photosynthesis and respiration, many students in the pretest showed misconception on the point of time and location at which botanic photosynthesis and respiration occur, botanic nutrient, the role of a leaf in photosynthesis, and the relation between botanic photosynthesis and respiration, but after teaching based on Driver's conceptual change model, their misconceptions on photosynthesis and respiration were rectified to a high degree.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.901-921
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• 2008

The purpose of this study was to investigate elementary teachers' views on the barriers in implementing inquiry-based instruction in science education. For this, semi-structured in-depth interviews were performed with 22 elementary school teachers who have served for more than five years in the Gyeonggi province. The interview questions were developed through triangulation of Seidman's phase to achieve reliability in the interview data, then interview questions were modified and completed through an analytic induction method in pre-interviews. In-depth interviews were performed individually and all the interviews were recorded. The data of teachers' views on the barriers were categorized and analyzed into external and internal factors of teachers. The study found that the external factors referred by teachers included the following; the lack of a unit time, lack of materials and equipments, too many students in a class, problems in science curriculum management, difficulty in the assessment of students' inquiry activities, the students' learning, lack of opportunities for teaching inquiry activities, harmfulness of accidents, and so on. Internal factors included the following; lack of preparation for inquiry activities, lack of self-confidence, lack of patience, and so on. The various barriers presented and their causes were analyzed in detail, and possible efforts in activating inquiry activities in elementary science education were suggested.