• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.501-503
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• 2022

본 논문은 이동 노드간 클러스터링을 함에 있어 보다 효율적인클러스터링을 제공하고 유지하기 위한 딥러닝의 자율학습에 따른 군집적 알고리즘을 제안한다. 대부분의 클러스터링 군집데이터를 처리함에 있어 상호관계에 따른 분류체계가 제공된다. 이러한 경우 새롭게 입력되거나 변경된 데이터가 비교정보에서 오염된 정보로 분류될 경우 기존 분류된 클러스터링으로부터 오염된 정보로 이해되어 군집성을 저하시키는 요인으로 작용 할 수가 있다. 본 논문에서는 이러한 상황정보를 이해하고 클러스터링을 유지할 수 있는 자율학습기반의 학습 모델을 제시 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.612-614
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• 2022

This paper propose a clustering algorithm for mobile nodes that possible more efficient clustering using context-aware attribute information in adaptive learning. In typically, the data will be provided to classify interrelationships within cluster properties. If a new properties are treated as contaminated information in comparative clustering, it can be treated as contaminated properties in comparison clustering. In this paper, To solve this problems in this paper, we have new present a context-awareness learning based model that can analyzes the clustering attributed parameters from the node properties using accumulated information properties.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.271-273
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• 2021

본 논문은 이동노드의 클러스터링내에서 보다 효율적인클러스터링을 제공하고 유지하기위한 딥러닝의 선형회귀적 적응적 보정가중치에 따른 군집적 알고리즘을 제안한다. 대부분의 클러스터링 군집데이터를 처리함에 있어 상호관계에 따른 분류체계가 제공된다. 이러한 경우 이웃한 이동노드중 목적노드와는 연결가능성이 가장높은 이동노드를 클러스터내에서 중계노드로 선택해야 한다. 본 연구에서는 이러한 상황정보를 이해하고 동적이동노드간 속도와 방향속성정보간의 상관관계의 친밀도를 고려한 자율학습기반의 회귀적 모델에서 적응적 가중치에 따른 분류를 제시한다. 본 논문에서는 이러한 상황정보를 이해하고 클러스터링을 유지할 수 있는 자율학습기반의 적응적 가중치에 따른 딥러닝 모델을 제시 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.942-948
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• 2020

In this paper, we propose a clustered algorithm that possible more efficient user distinction within clustering using context-aware attribute information. In typically, the data provided to classify interrelationships within cluster information in the process of clustering data will be as a degrade factor if new or newly processing information is treated as contaminated information in comparative information. In this paper, we have developed a clustering algorithm that can extract user's recognition information to solve this problem in using K-means algorithm. The proposed algorithm analyzes the user's clustering attributed parameters from user clusters using accumulated information and clustering according to their attributes. The results of the simulation with the proposed algorithm showed that the user management system was more adaptable in terms of classifying and maintaining multiple users in clusters.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.156-170
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• 2023

Objectives: This study conducted practical training to improve the proper usage of personal protective equipment(PPE), which greatly impacts workplace safety and health management. Personal protective equipment education was conducted through active participation, without theoretical modules, and aimed to identify the effects of repeated practical education and determine ways to increase participant satisfaction. Methods: Study data were analyzed using the IBM SPSS Statistics ver.29 software. First, participants' general characteristics were analyzed with frequency analysis. Second, the normality and equality of variances (Leven's test) were tested for the dependent variables prior to statistical analyses to determine the use of parametric tests. In general, normality is assumed when the sample size is 30 or more per the central limit theorem (Park et al., 2014). As our sample size of health management workers was 43, normality can be assumed. However, to ensure rigor of the study, we examined skewness and kurtosis. The results confirmed that the data were normally distributed. Third, the effects of repeated PPE training were analyzed using paired t-tests. Fourth, differences in satisfaction with PPE training according to the safety and health job position and safety and health certification were analyzed with t-test and Welch's t-test. For parameters that did not meet the assumption of equal variances, the Welch's t-test was performed. Results: Repeated PPE training improved the educational outcomes, and the improvements were significant in the 1st and 2nd respiratory PPE and safety and hygiene PPE training evaluations (p<.001). In terms of safety and health job position, repeated training led to improvements in educational outcomes, with significant improvements observed among supervisors and specialized health management institution workers in the 1st and 2nd training evaluations (p<.005). In terms of safety certification, repeated training led to improvements in educational outcomes, with significant improvements observed among both certified and non-certified individuals (p<.005). Regarding satisfaction with PPE training according to safety and health job positions, specialized health management institution workers showed greater satisfaction than supervisors, with significant differences in the satisfaction for expertise of lecture, work relevance, and lecturer's attitude (p<.001). Regarding satisfaction with PPE training according to safety and health certification, satisfaction was higher among certified individuals, with significant differences in satisfaction for work relevance and lecture attitude (p<.05) Conclusions: PPE education should be recommended to be provided as practical training. Repeated training can enhance educational outcomes for individuals with inadequate knowledge and understanding of PPE prior to education. For individuals with high levels of pre-existing knowledge and understanding of PPE, the results show that various training experiences should be provided to enhance their satisfaction. Therefore, it suggests that the workplace should actively seek educational media and methods to acquire expertise and skills in wearing personal protective equipment and improve the ability to use