• 스마트미디어저널
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• 제12권4호
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• pp.59-59
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• 2023

• 스마트미디어저널
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• 제12권4호
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• pp.58-58
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• 2023

• 스마트미디어저널
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• 제12권9호
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• pp.171-171
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• 2023

• 스마트미디어저널
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• 제12권9호
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• pp.172-172
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• 2023

• 스마트미디어저널
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• 제12권5호
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• pp.122-122
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• 2023

• 스마트미디어저널
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• 제8권3호
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• pp.17-22
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• 2019

Signature-based intrusion systems use intrusion detection rules for detecting intrusion. However, writing intrusion detection rules is difficult and requires considerable knowledge of various fields. Attackers may modify previous attempts to escape intrusion detection rules. In this paper, we deal with the problem of detecting modified attacks based on previous intrusion detection rules. We show a simple method of reporting approximate occurrences of at least one of the network intrusion detection rules, based on q-grams and the longest increasing subsequences. Experimental results showed that our approach could detect modified attacks, modeled with edit operations.

• 스마트미디어저널
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• 제13권1호
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• pp.24-35
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• 2024

The combination of Artificial Intelligence and the Internet of Things (AIoT) has gained significant popularity. Deep neural networks (DNNs) have demonstrated remarkable success in various applications. However, deploying complex AI models on embedded boards can pose challenges due to computational limitations and model complexity. This paper presents an AIoT-based system for smart parking lots using edge devices. Our approach involves developing a detection model and a decision tree for occupancy status classification. Specifically, we utilize YOLOv5 for car license plate (LP) detection by verifying the position of the license plate within the parking space.

• 스마트미디어저널
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• 제9권3호
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• 스마트미디어저널
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• 제9권3호
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• pp.116-121
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• 2020

Digital Information Display와 KIOSK는 전용 컨텐츠의 개발 비용으로 인한 초기 도입 비용 및 유지 비용과 제품의 특성으로 인해 설치 비용이 높다는 문제가 있다. 이러한 문제를 해결하기 위해 오픈소스 하드웨어 및 이벤트 기반 논 블로킹 I/O 알고리즘을 사용하여 음성 전송 시스템을 설계하고 구현하였다. 제안하는 오픈 하드웨어를 통한 음성송출 시스템은 시스템 초기 도입 비용과 유지 보수비용이 저렴하고, 다양한 형태로 활용할 수 있어서 정보 취약 계층의 정보에 대한 접근성을 향상할 수 있다.

• 스마트미디어저널
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• 제9권3호
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• pp.9-17
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• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.