• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.83-89
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• 2021

In this paper, we propose an algorithm that provide services to consumers who want to conduct business by scientifically and systematically analyzing the number of pedestrians in a specific area over a specific period of time. In this paper, we proposed the algorithm to analyze the commercial area using the pedestrian-detect algorithm in the particular region using YOLO, one of the deep learning techniques. And with one image per minute in the images, the number of pedestrians is identified and this information is used for the analysis of business district on interesting area and time, systematically and objectively.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.17-24
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• 2021

There is a problem that unknown paintings are sophisticated in the level of forgery, making it difficult for even experts to determine whether they are genuine or counterfeit. These problems can be suspected of forgery even if the genuine product is submitted, which can lead to a decline in the value of the work and the artist. To address these issues, in this paper, we propose a system to classify chromaticity data among extracted data through objective analysis into quadrants, extracting comparisons and intersections, and estimating authors of unknown paintings using XRF and hyperspectral spectrum data from corresponding points.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.43-53
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• 2019

BigCloneBench has recently been used for performance evaluation of code clone detection tool using machine learning. However, since BigCloneBench is not a benchmark that is optimized for machine learning, incorrect learning data can be created. In this paper, we have shown through experiments using machine learning that the set of Type-4 clone methods provided by BigCloneBench can additionally be found. Experimental results using Tree-Based Convolutional Neural Network show that our proposed method is effective in improving BigCloneBench's dataset.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.79-86
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• 2019

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.95-100
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• 2019

In this paper, we propose a simple hardware reporting method for errors in soft-RAID systems of Linux OS. Compared with other reporting methods, the proposed method displays error status intuitively without any additional access process such as log-in process or home-page access. In particular, the server actively displays the error status, so the administrator can take immediate action. In order to confirm the effectiveness of the proposed method, the experimental circuit was constructed and the experimental results showed that the error was actively displayed when an error occurred in the storage device. As such, a soft-RAID system can perform almost the same function as a hardware RAID system, thereby ensuring server data reliability at low cost.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.57-62
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• 2022

This study was conducted with the purpose of listening to the explanations of artworks in the non-face-to-face class and confirming the learner's feelings as a result of the class. The proposed system listens to the explanation of the artwork, inputs the learner's emotions with a dedicated key, and expresses the result in music. To this end, the direction of the non-face-to-face art appreciation class model using the emotion key was set, and based on this, a system for non-face-to-face art appreciation was constructed. The learner will use the 'smart device using the emotion key' proposed in this study to listen to the explanation of the artwork and to input the emotion for the question presented. Through the proposed system, learners can express their emotional state in online art classes, and instructors receive the results of class participation and use them in various ways for educational analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.737-738
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• 2024

본 연구는 사용자의 감정을 인식하고, 개인화된 행동을 학습하는 AI 반려동물 시스템을 제안한다. DQN 을 이용한 강화학습을 통해 사용자의 피드백에 따라 행동을 변화시키며, 보다 자연스럽고 흥미로운 상호작용을 가능하게 한다. 이를 통해 기존의 정형화된 로봇 반려동물의 한계를 극복하고 사용자에게 맞춤형 경험을 제공하는 AI 반려동물 개발에 기여하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.559-561
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• 2022

인공지능 시대에 들어서면서 개인 맞춤형 환경을 제공하기 위하여 사람의 감정을 인식하고 교감하는 기술이 많이 발전되고 있다. 사람의 감정을 인식하는 방법으로는 얼굴, 음성, 신체 동작, 생체 신호 등이 있지만 이 중 가장 직관적이면서도 쉽게 접할 수 있는 것은 표정이다. 따라서, 본 논문에서는 정확도 높은 얼굴 감정 식별을 위해서 Convolution Block Attention Module(CBAM)의 각 Gate와 Residual Block, Skip Connection을 이용한 Identity- CBAM Module을 제안한다. CBAM의 각 Gate와 Residual Block을 이용하여 각각의 표정에 대한 핵심 특징 정보들을 강조하여 Context 한 모델로 변화시켜주는 효과를 가지게 하였으며 Skip-Connection을 이용하여 기울기 소실 및 폭발에 강인하게 해주는 모듈을 제안한다. AI-HUB의 한국인 감정 인식을 위한 복합 영상 데이터 세트를 이용하여 총 6개의 클래스로 구분하였으며, F1-Score, Accuracy 기준으로 Identity-CBAM 모듈을 적용하였을 때 Vanilla ResNet50, ResNet101 대비 F1-Score 0.4~2.7%, Accuracy 0.18~2.03%의 성능 향상을 달성하였다. 또한, Guided Backpropagation과 Guided GradCam을 통해 시각화하였을 때 중요 특징점들을 더 세밀하게 표현하는 것을 확인하였다. 결과적으로 이미지 내 표정 분류 Task에서 Vanilla ResNet50, ResNet101을 사용하는 것보다 Identity-CBAM Module을 함께 사용하는 것이 더 적합함을 입증하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.105-112
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• 2024

In contemporary society, the expression of emotions and self-reflection are considered pivotal factors with a positive impact on stress management and mental well-being, thereby highlighting the significance of journaling. However, traditional journaling methods have posed challenges for many individuals due to constraints in terms of time and space. Recent rapid advancements in chatbot and emotion analysis technologies have garnered significant attention as essential tools to address these issues. This paper introduces an artificial intelligence chatbot that integrates the GPT-3 model and emotion analysis technology, detailing the development process of a system that automatically generates journals based on users' chat data. Through this system, users can engage in journaling more conveniently and efficiently, fostering a deeper understanding of their emotions and promoting positive emotional experiences.