• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.23-30
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• 2020

In this study, the relationship between natural gas (NG) data and gas-related environmental elements was performed using machine learning algorithms to predict the level of gas leakage risk without directly measuring gas leakage data. The study was based on open data provided by the server using the IoT-based remote control Picarro gas sensor specification. The naturel gas leaks into the air, it is a big problem for air pollution, environment and the health. The proposed method is multivariate outlier removing method based Random Forest (RF) classification for predicting risk of NG leak. After, unsupervised k-means clustering, the experimental dataset has done imbalanced data. Therefore, we focusing our proposed models can predict medium and high risk so best. In this case, we compared the receiver operating characteristic (ROC) curve, accuracy, area under the ROC curve (AUC), and mean standard error (MSE) for each classification model. As a result of our experiments, the evaluation measurements include accuracy, area under the ROC curve (AUC), and MSE; 99.71%, 99.57%, and 0.0016 for MOL_RF respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1679-1686
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• 2019

The main purpose of this research is to propose an approach for solving the packet loss problem by quickly adapting to topology change when nodes move in RPL-based IoT environment. In order to enhance mobility, every node is aware of the mobility of its neighbor nodes and quantifies the mobility level based on the number of control messages and all received packets. According to the mobility level, the DIO timer is changed. The proposed approach allows nodes to change their DIO timers according to their mobility levels to adapt topology changes and update paths to the sink. The performance of the proposed approach is evaluated using a Contiki-based Cooja simulator in various moving speeds. The simulation results show that the proposed approach copes with mobility scenarios better than the standard RPL by ascertaining that the packet delivery ratio is improved by 31.03%.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.35-44
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• 2021

Previous studies have studied short videos, short form content, snack culture and so on, but few studies have been conducted on the form of summary content that compressing and summarizing the original content. This study aims to contribute to the revitalization of the summary content market by exploring ways to enhance user satisfaction through analysis of the YouTube summary content features and the credibility of content providers that bring about flow and satisfaction of YouTube summary content users. The survey was conducted on 202 people who have watched YouTube summary contents for finding out the effects of YouTube summary contents features and content provider credibility on the details of flow. As a result, only entertainment had a significant impact on all flow details. This study is of academic significance in that it defines the features of YouTube summary contents, and has practical significance in that it suggests what direction the summary content should have in order to arouse user satisfaction in future.

• Journal of Convergence for Information Technology
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• v.11 no.7
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• pp.21-30
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• 2021

With the development of IoT technology, various cloud computing-based services such as smart cars, smart healthcare, smart homes, and smart farms are expanding. With the advent of a new environment, various problems continue to occur, such as the possibility of exposure of important information such as personal information or company secrets, financial damage cases due to hacking, and human casualties due to malicious attack techniques. In this paper, we propose a message communication protocol for smart home-based secure communication and user data protection. As a detailed process, secure device registration, message authentication protocol, and renewal protocol were newly designed in the smart home environment. By referring to the security requirements related to the smart home service, the stability of the representative attack technique was verified, and as a result of performing a comparative analysis of the performance, the efficiency of about 50% in the communication aspect and 25% in the signature verification aspect was confirmed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1279-1289
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• 2020

To securely protect users' sensitive information and national secrets, the importance of cryptographic modules has been emphasized. Currently, many companies and national organizations are actively using cryptographic modules. In Korea, To ensure the security of these cryptographic modules, the cryptographic module has been verified through the Korea Certificate Module Validation Program(KCMVP). Most of the domestic cryptographic modules are CPU-based software (S/W). However, CPU-based cryptographic modules are difficult to use in servers that need to process large amounts of data. In this paper, we propose an S/W cryptographic module that provides a high-speed operation using GPU. We describe the configuration and operation of the S/W cryptographic module using GPU and present the changes in the cryptographic module security requirements by using GPU. In addition, we present the performance improvement compared to the existing CPU S/W cryptographic module. The results of this paper can be used for cryptographic modules that provide cryptography in servers that manage IoT (Internet of Things) or provide cloud computing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.722-728
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• 2020

Digital image processing has been applied in a wide range of fields due to the development of IoT technology and plays an important role in data processing. Various techniques have been proposed to remove such noise, but the conventional impulse noise canceling methods are insufficient to remove noise of edge components of an image, and have a disadvantage of being greatly affected by random impulse noise. Therefore, in this paper, we propose an algorithm that effectively removes edge component noise in random impulse noise environment. The proposed algorithm calculates the threshold value by determining the noise level and switches the filtering process by comparing the reference value with the input pixel value. The proposed algorithm shows good performance in the existing method, and the simulation results show that the noise is effectively removed from the edge of the image.

• Journal of Internet of Things and Convergence
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• v.7 no.1
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• pp.55-62
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• 2021

The world is changing rapidly as the Internet spreads and various smart devices appear. High-performance PCs and high-speed communication networks are rapidly spreading in every home, and all kinds of the internet sites are emerging. In particular, the high education enthusiasm of Korean parents adds to this, and the ratio of the internet users among teenagers is exploding every day. In the case of adolescents, most of them use the Internet for online games, indicating that online games are the main cause of the internet addiction. This study was conducted using a questionnaire for male and female high school students using the Internet, and demographic and sociological characteristics were used only as basic data. In this study, as much as parents, students and teachers think, the results of the internet addiction type analysis according to academic achievement in humanities high school students are to be investigated to determine whether internet use has an effect on academic achievement.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.96-101
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• 2021

This paper presents a machine learning model that predicts stroke risks in atrial fibrillation patients using public big data. As the training data, 68 independent variables including demographic, medical history, health examination were collected from the Korean National Health Insurance Service. To predict stroke incidence in patients with atrial fibrillation, we applied deep neural network. We firstly verify the performance of conventional statistical models (CHADS2, CHA2DS2-VASc). Then we compared proposed model with the statistical models for various hyperparameters. Accuracy and area under the receiver operating characteristic (AUROC) were mainly used as indicators for performance evaluation. As a result, the model using batch normalization showed the highest performance, which recorded better performance than the statistical model.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1904-1913
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• 2021

With the proliferation of smart phones and the development of IoT technology, it has become possible to collect personal data for public purposes. However, users are afraid of voluntarily providing their private data due to privacy issues. To remedy this problem, mainly three techniques have been studied: data disturbance, traditional encryption, and homomorphic encryption. In this work, we perform simulations to compare them in terms of accuracy, message length, and computation delay. Experiment results show that the data disturbance method is fast and inaccurate while the traditional encryption method is accurate and slow. Similar to traditional encryption algorithms, the homomorphic encryption algorithm is relatively effective in privacy preserving because it allows computing encrypted data without decryption, but it requires high computation costs as well. However, its main cost, arithmetic operations, can be processed in parallel. Also, data analysis using the homomorphic encryption needs to do decryption only once at any number of data.