• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.97-106
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• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.157-162
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• 2024

This study was conducted to implement Joseon Dynasty conversational style using the ChatGPT API to enhance the immersion of games set in the Joseon era. The research focuses on interactions between middle-class players and other classes. Two methods were employed: learning the dialogues from historical dramas set in the Joseon Dynasty and learning the sentence endings typical of the period. The method of learning sentence endings was rated higher based on self-evaluation criteria. Reflecting this, prompts were constructed to represent NPC dialogues in the game settings of the Joseon era. Additionally, a method was proposed for creating various NPC prompts using prompt combination techniques. This study can serve as a reference for NPC dialogue creation in games set in the Joseon Dynasty.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.246-252
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• 2018

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.668-676
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• 2016

This study reviewed the planning characteristics of contemporary Japanese middle school architecture on which related studies are insufficient, aiming to obtain new ideas for planning Korean middle school facilities. Fourteen case schools built after 1990s were selected and analyzed. They were divided into learning-living space and other major spaces. The planning characteristics of the case schools are summarized as follows 1) The case schools were classified into two categories, departmentalized classroom type (D type) and usual with variation type (UV type) by school system. These categories can also be the classification standard for basic architectural characteristics in learning and living space of case schools. 2) D type case schools have departmentalized classrooms, home base, media space and teacher's space for learning-living space. D type case schools are divided into 'attached-to-classroom type' and 'separate type' depending on the adjacency of the home base and departmentalized classroom. 3) UV type case schools have multipurpose space around the classroom for learning-living space and can be divided into two types, i.e., 'directly adjacent' and 'separate', depending on the connectivity to classroom of multipurpose room. 4) Specialized classrooms are designed to have the openness to the public and the own characteristics of school subjects strengthened and show the spatial differentiation with connected ancillary spaces. 5) Libraries are designed as complex zones grouped with computer labs, audio visual rooms and multipurpose halls not as a single room and as open plan not with a closed wall. 6) The gymnasium is the basic sports facility with a martial arts room and outdoor pool, which are for after-school activities as well as physical education class. 7) The terrace, balcony and outdoor stairs are frequently used architectural vocabularies as diverse outdoor spaces with a variety of functions.

• Journal of the Korean earth science society
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• v.29 no.6
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• pp.506-519
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• 2008

This article explores the potential learning materials and methods of science practice from exhibits, and how those are presented in natural history museums as a feasible science inquiry community. The idea of science inquiry community was offered as a form of science practice that ended with science learning. A grasp of 'scientific practice to learning' is understood as a way to conceive scientific methods as well as facts and understanding knowledge. To get educational implications on the scientific practice of 'earthquake' as a socioscientific topic in the communities, we analyzed 1) the relationship between earth science curriculum and exhibits related to 'earthquake', 2) the educational goals and intentions of educators, and 3) the characteristics of the exhibits in the American Museum of Natural History and in the Smithsonian National Museum of Natural History. The results of this study showed that those museums presented the exhibits consisting of various and practical cases and events of 'earthquakes' as a socioscientific topic related to their curriculum. At the target museum, it was clearly stated that the pursuing educational goals focused on relations with local interests and socioscientific issues. For making earthquakes relevant to visitors, delivering lived experiences with raw data and interactive media was emphasized in exhibit characteristics.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.240-250
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• 2012

For this study, 373 students of H cyber university were chosen to conduct a survey in the spring semester of 2011. The result of this study through structural equation modeling analysis was as follows: First, organizational support significantly affected perceived isolation. Second, organizational support and perceived isolation significantly affected satisfaction. Third, perceived isolation and satisfaction significantly affected learning persistence, while organizational support didn't. In addition, satisfaction was verified as a mediating variable between organizational support, satisfaction, and learning persistence, and satisfaction was verified as a mediating variable between perceived isolation, organizational support and learning persistence. These results imply that perceived isolation and organizational support should be considered for the design and development strategies of instructional courses in order to enhance satisfaction and learning persistence of students in cyber educational environment.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.495-505
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• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.