• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.401-410
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• 2021

Recently, as cyber attacks targeting industrial control systems increase, various studies are being conducted on the detection of abnormalities in industrial processes. Considering that the industrial process is deterministic and regular, It is appropriate to determine abnormality by comparing the predicted value of the detection model from which normal data is trained and the actual value. In this paper, HAI Datasets 20.07 and 21.03 are used. In addition, an ensemble model is created by combining models that have applied different time steps to Gated Recurrent Units. Then, the detection performance of the single model and the ensemble recurrent neural networks model were compared through various performance evaluation analysis, and It was confirmed that the proposed model is more suitable for abnormal detection in industrial control systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.59-64
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• 2020

Recently, many difficulties have been faced in all parts of the world due to the impact of COVID-19. Personally, household income is decreasing sharply as many jobs disappear, and economically, many SMEs are increasingly going bankrupt. It is known that this phenomenon is highly likely to continue for the time being. In such a situation, the smart factory support project provides opportunities for difficult SMEs to improve productivity and change the corporate structure. In this study, the current status of smart factory promotion was examined, and problems occurring in the process of promoting smart factory support projects were identified. The improvement plans were derived so that more efficient projects could be promoted in the future.

• 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 the Korea Society of Computer and Information
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• v.27 no.2
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• pp.145-151
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• 2022

In the study, the author aims to investigate the social media platform that helps promotion of stress relieve and social abilities that exist in lifestyle of persons living in the modern times and perform literature studies of characteristics of stress in each life cycle and social media thereof. In results, it is concluded that persons living in the modern times are under various stress during adolescence, middle age and elderly periods of the entire life cycle and especially, in Korea, stress index is increasing rapidly. To resolve stress, as an alternative, internet based social medial platform can be used to achieve various information supply and access. It is suggested that the development and accessibility of platforms for each inclination should be made easy in line with each inclination and desire for complex and diverse personal inclinations and individualistic activities, and related research should be continued.

• Journal of Distribution Science
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• v.20 no.8
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• pp.93-103
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• 2022

Purpose: Online shopping festivals have quickly become the newest trend in online shopping worldwide due to the COVID-19 pandemic. This has led to marketing distribution channels that traditionally emphasized traditional techniques having turned to electronic commerce platforms. Although the pandemic scenario encourages online purchasing, other factors, such as the influence of participation intention to shop during the Online Shopping Festival, must also be considered. Research design, data and methodology: Multiple linear regression analysis was used to test the hypothesis based on data from 121 respondents who are actively involved with online shopping activities in Klang Valley, Selangor. Results: The results of this study show that promotion categories and the perceived influence of mass participation have a significant influence on participation intention. Meanwhile, the perceived temptation of price promotion and perceived fun promotional activities did not significantly influence participation intention. Conclusions: Theoretically, this study contributes to the literature by using the Theory of Planned Behavior and Stimulus-Response models to explain the factors that drive participation intention for online shopping. In practice, this study attracts and encourages customers to shop during the festival day because various attractive promotions are offered by sellers in Malaysia.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.63-70
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• 2019

We extract the symptom of panic disorder from the context awareness environment. It extracts body context information through natural movement that exists in everyday life and uses a component of panic disorder. The ontology theory can be used to provide information on the degree of symptoms of panic disorder through inference process. For the components of panic disorder to the information processing based on ontology are defined as Classes. Panic disorder index is expressed through ontology modeling so that the condition of panic disorder can be known. The derivation of panic disorder component and panic disorder index will enable context awareness based information service for panic disorder. The context information is periodically synchronized with the context awareness on based device. Panic disorder can be used to improve the lifestyle of panic disorder.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.49-60
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• 2023

The amount of data generated by electronic systems through e-commerce, social networks, and data computation has risen. However, the security of data has always been a challenge. The problem is not with the quantity of data but how to secure the data by ensuring its confidentiality and privacy. Though there are several research on cloud data security, this study proposes a security scheme with the lowest execution time. The approach employs a non-linear time complexity to achieve data confidentiality and privacy. A symmetric algorithm dubbed the Non-Deterministic Cryptographic Scheme (NCS) is proposed to address the increased execution time of existing cryptographic schemes. NCS has linear time complexity with a low and unpredicted trend of execution times. It achieves confidentiality and privacy of data on the cloud by converting the plaintext into Ciphertext with a small number of iterations thereby decreasing the execution time but with high security. The algorithm is based on Good Prime Numbers, Linear Congruential Generator (LGC), Sliding Window Algorithm (SWA), and XOR gate. For the implementation in C, thirty different execution times were performed and their average was taken. A comparative analysis of the NCS was performed against AES, DES, and RSA algorithms based on key sizes of 128kb, 256kb, and 512kb using the dataset from Kaggle. The results showed the proposed NCS execution times were lower in comparison to AES, which had better execution time than DES with RSA having the longest. Contrary, to existing knowledge that execution time is relative to data size, the results obtained from the experiment indicated otherwise for the proposed NCS algorithm. With data sizes of 128kb, 256kb, and 512kb, the execution times in milliseconds were 38, 711, and 378 respectively. This validates the NCS as a Non-Deterministic Cryptographic Algorithm. The study findings hence are in support of the argument that data size does not determine the execution.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.335-343
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• 2019

Healthy living is an essential element of human happiness. Quality eating provides the basis for life, and the health of livestock, which provides meat and dairy products, has a direct impact on human health. In the case of calves, diarrhea is the cause of all diseases.In this paper, we use a sensor to measure calf 's biometric data to diagnose calf diarrhea. The collected biometric data is subjected to a preprocessing process for use as meaningful information. We measure calf birth history and calf biometrics. The ontology is constructed by inputting environmental information of housing and biochemistry, immunity, and measurement information of human body for disease management. We will build a knowledge base for predicting calf diarrhea by predicting calf diarrhea through logical reasoning. Predict diarrhea with the knowledge base on the name of the disease, cause, timing and symptoms of livestock diseases. These knowledge bases can be expressed as domain ontologies for parent ontology and prediction, and as a result, treatment and prevention methods can be suggested.