• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.301-307
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• 2021

In addition to the revival of the Internet of Things, embedded systems, which are at the core of the Internet of Things, require intelligent control as things change. Embedded systems, however, are heavily constrained by resources such as hardware, memory, time and power. When changes are needed to firmware in an embedded system, flash Memory must be initialized and the entire firmware must be uploaded again. Therefore, it is time- and energy-efficient in that areas that do not need to be modified must also be initialized and rewritten. In this paper, we propose how to upload firmware in installments to each sector of flash memory so that only firmware can be replace the firmware in the parts that need to be modified when the firmware needs to be modified. In this paper, the proposed method was evaluated using real target board, and as a result, the time was reduced by about half.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.1-10
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• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.475-483
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• 2018

The purpose of this study to understand the vocational education of China and to analyze the developing process of vocational education in China. Recently, the major countries of the world of modern society has its government leaders is how much to foster creativity and the rise and fall depending on whether they really value is determined claims. There are 1327 Tertiary Vocational Colleges, with 10 million students in 2015. Together with 14million secondary vocational education students, China owns the largest scale of vocational education in the world. China has not fully established a modern market. Under the economy, the enterprise was the administrative adjunct of the state, and the enterprise was the social and political production function, and the political ethics prevailed that ethics. Literature review and historial approach were utilized as the methodology for this study. The system of vocational education in China is composed of elementary, secondary, and higher stage. The vocational education in China has been developed flexibly along with the social change while keeping the main philosophy of Chinese socialism. The main factors to bring about the change of vocational education in China is, political and philosophical, economical change.

• Convergence Security Journal
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• v.22 no.4
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• pp.169-178
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• 2022

In general, organizations operating multi-domain networks find it difficult to represent and manage multiple domain net works on a single screen space. Instead, most of them are managed with multiple screens visualizing network topology by domain or partitioning one screen area into multiple domains. We propose an efficient method to visualize the topology using only minimal connection information between domain-agnostic nodes in this work. This method visualizes the topology by utilizing centrality indices representing the influence of nodes in the network. Furthermore, the method dynamically segments the entire node's display area using virtual Root nodes to auto-separate domains and weights of child nodes and placing nodes in 3D space. Thus, although it is a straightforward method, the multi-domain network topology can be visualized with only minimal connection information between nodes.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.947-960
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• 2023

In Korea, traditional paper promissory notes are currently undergoing a transformation, being gradually replaced by electronic notes. This transformation is being steered under the Korea Financial Telecommunications Institute, a trusted authority. However, existing electronic systems have security vulnerabilities, including the risk of hacking and internal errors within the institute. To this end, we have defined a novel anonymous electronic promissory note system based on blockchain. We have constructed a concrete protocol and conducted security analysis of our protocol. Note that, in our protocol, every note information is committed so that the note remains undisclosed until the point of payment. Once the note information becomes public on the blockchain, it enables the detection of illicit activities, such as money laundering and tax evasion. Furthermore, our protocol incorporates a feature of split endorsement, which is a crucial functionality permitted by the Korean electronic note system. Consequently, our proposed protocol is suitable for practical applications in financial transactions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.143-156
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• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• Journal of IKEEE
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• v.28 no.3
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• pp.352-359
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• 2024

This paper proposes a CNN-ViT hybrid model that automatically evaluates the aesthetic quality of images by combining local and global features. In this approach, CNN is used to extract local features such as color and object placement, while ViT is employed to analyze the aesthetic value of the image by reflecting global features. Color composition is derived by extracting the primary colors from the input image, creating a color palette, and then passing it through the CNN. The Rule of Thirds is quantified by calculating how closely objects in the image are positioned near the thirds intersection points. These values provide the model with critical information about the color balance and spatial harmony of the image. The model then analyzes the relationship between these factors to predict scores that align closely with human judgment. Experimental results on the AADB image database show that the proposed model achieved a Spearman's Rank Correlation Coefficient (SRCC) of 0.716, indicating more consistent rank predictions, and a Pearson Correlation Coefficient (LCC) of 0.72, which is 2~4% higher than existing models.

• Journal of Nutrition and Health
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• v.42 no.8
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• pp.682-690
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• 2009

The purpose of this study is to analyse the relation between dietary habit and nutrition knowledge, and ADHD (Attention Deficit Hyperactivity Disorder) in the middle school students in Seoul, Korea. Total study subjects were 631 students, 51.8% was male and 48.2% was female. In the assessment of predisposition of ADHD, 93% was normal group and 7% was ADHD group. As for the gender in ADHD group, 56.8% was male and 43.2% was female. Normal group showed the higher breakfast consumption rate than ADHD group (p < 0.05). Dietary habits were better in normal group than ADHD group. Nutrition knowledge scores of normal group was 7.38 out of 15 and scores of ADHD group was 5.77 out of 15 (p < 0.01). The nutrition knowledge score and the dietary habits score showed a positive correlation (p < 0.01). The nutrition knowledge score and snack meal purchasing frequency showed a negative correlation (p < 0.05). There are significant negative correlation between attention deficit hyperactivity score and nutrition knowledge score (p < 0.01). In conclusion, ADHD group showed lower level of nutrition knowledge and worse dietary habits than the normal group.