• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.3
• /
• pp.125-132
• /
• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1809-1816
• /
• 2021

In order to identify the latest research trends using data related to national R&D projects and to produce and utilize meaningful information, the application of automatic classification technology was also required in the national R&D information service, so we conducted research to automatically classify and recommend research field. About 450,000 cases of national R&D project data from 2013 to 2020 were collected and used for learning and evaluation. A model was selected after data pre-processing, analysis, and performance analysis for valid data among collected data. The performance of Word2vec, GloVe, and fastText was compared for the purpose of deriving the optimal model combination. As a result of the experiment, the accuracy of only the subcategories used as essential items of task information is 90.11%. This model is expected to be applicable to the automatic classification study of other classification systems with a hierarchical structure similar to that of the national science and technology standard classification research field.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.3
• /
• pp.9-14
• /
• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.12
• /
• pp.1855-1862
• /
• 2022

Various indoor location-based services can be provided through indoor presence determination and indoor positioning technology using Beacon. However, since the BLE signal advertised by the beacon has an unstable RSSI due to problems such as multi-path fading, it is difficult to guarantee the accuracy of indoor presence determination. In this paper, data were collected while the classroom door was open to ensure accuracy in various situations. Based on the collected data, we propose an indoor presence determination method considering the characteristics of the signal. The proposed method uses support vector machine, showed about 10% accuracy improvement compared to the results using raw RSSI only. This method has the advantage of being able to accurately determine indoor presence with only one receiver. It is expected that the proposed method can implement a low-cost system for determining indoor presence with high accuracy.

• Convergence Security Journal
• /
• v.22 no.1
• /
• pp.29-38
• /
• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.17-25
• /
• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.1
• /
• pp.21-26
• /
• 2023

Knowledge distillation (KD) is a model lightening technology that transfers the knowledge of deep models to light models. Most KD methods have been developed for classification models, and there have been few KD studies in the field of super-resolution (SR). In this paper, various KD methods are applied to an SR model and their performance is compared. Specifically, we modified the loss function to apply each KD method to the SR model and conducted an experiment to learn a student model that was about 27 times lighter than the teacher model and to double the image resolution. Through the experiment, it was confirmed that some KD methods were not valid when applied to SR models, and that the performance was the highest when the relational KD and the traditional KD methods were combined.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.6
• /
• pp.39-51
• /
• 2024

This paper proposes a novel approach to enhance the security of container-based systems by analyzing system calls to dynamically detect race conditions without modifying the kernel. Container escape attacks allow attackers to break out of a container's isolation and access other systems, utilizing vulnerabilities such as race conditions that can occur in parallel computing environments. To effectively detect and defend against such attacks, this study utilizes eBPF to observe system call patterns during attack attempts and employs a AdaBoost model to detect them. For this purpose, system calls invoked during the attacks such as Dirty COW and Dirty Cred from popular applications such as MongoDB, PostgreSQL, and Redis, were used as training data. The experimental results show that this method achieved a precision of 99.55%, a recall of 99.68%, and an F1-score of 99.62%, with the system overhead of 8%.

• The Journal of Korean Association of Computer Education
• /
• v.17 no.2
• /
• pp.11-19
• /
• 2014

Presently the instructional model for internet ethics education is modeled after the integrated morality. The model is based on the assumption that ethical awareness will lead to ethical activities which is based on the theory that cognition is correlated to the behavioral domains. But the side effects of the information society in the cyber space increased even when the education for the awareness of ethics in the cyber space has been taught more aggressively than before. In this study, the relation of the cognition for information ethics and the ethical behavior in the cyber space was analyzed in order to find out the implications for the effective internet ethics education model. The tools used are the 'DIT (Defining Issues Test)' to measure the behavioral ability in the physical world, the Information Ethics Judgment to measure the behavioral ability in the cyber space, and the self diagnostic tool of 'Internet ethics awareness' to measure the level of cognitive knowledge for internet ethics. The correlation of three measures was analyzed. The results were college students' levels of ethics from three tools from are considerably low. Moral judgement and information ethics judgement were not correlated which means that the behavior in the physical world was not necessarily correlated to the behavior in the cyber space. The three measurements were not statistically significantly correlated. Therefore the cognitive awareness for the information ethics were not necessarily correlated to the ethical behavior in the cyber space. Ethical cognition and the moral behavior need to be taught with equal emphasis as they do not have strong correlation.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.11
• /
• pp.256-264
• /
• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.