• 정보보호학회논문지
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• 제32권5호
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• pp.1009-1017
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• 2022

로그 데이터는 정보 시스템의 주요 동작과 상태를 이해하고 판단하는 근거로 사용되어 왔으며, 여러 보안 분야 응용에서도 중요한 입력 데이터로 사용된다. 로그 데이터로부터 필요한 정보를 얻어 이를 근거로 의사 결정을 하고, 적절한 대응 방안을 취하는 것은 시스템을 보호하고 안정적으로 운영하는 데 있어 필수적인 요소이지만, 로그의 종류와 양이 폭발적으로 증가함에 따라 기존 도구들로는 효과적이고 효율적인 대응이 쉽지 않은 상황이다. 이에 본 연구에서는 자연어 처리 기반의 머신 러닝을 이용해 멀티 소스 이벤트 로그의 보안 심각도를 여러 단계로 분류하는 방법을 제안하였으며, 472,972건의 훈련 및 테스트 샘플을 이용하여 실험을 수행한 결과 99.59%의 정확도를 달성하였다.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.37-44
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• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

• Advances in Computational Design
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• 제7권2호
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• pp.139-151
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• 2022

The article is devoted to the actual problem of studying the possibilities of implementing personalization of the user interface in accordance with the personality psychotypes. The psychological aspect of user interface design tools is studied and the correspondence of their application to the manifestations of personality psychotypes is established. The results of the distribu-tion of attention of users of these categories on the course page of the educational platform are presented and the distribution of attention in accordance with the focus on educational material is analyzed. Individual features and personal preferences regarding the used design tools are described, namely the use of accent colors in interface design, the application of the prin-ciples of typographic hierarchy, and so on. In accordance with this, the prospects for implementing personalization of the user interface of the educational platform are described. The results of the study allow us to state the relevance of developing and applying personalization of the user interface of an educational platform to improve learning outcomes in accordance with the psychological impact of individual design tools, and taking into account certain features of user categories. The research is devoted to the study of user attention concentration using heatmaps, in particular based on eyetreking technology, we will investigate the distribution of user attention on the course page of an educational platform Ta redistribution of atten-tion in accordance with certain categories of personality psychotypes. The results of the study can be used to rearrange the LMS Moodle interface according to the user's psychotype to achieve the best concentration on the training material. The obtained data are the basis for developing effective user interfaces for personalizing educational platforms to improve the quality of the education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• 무역학회지
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• 제42권4호
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• pp.159-184
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• 2017

OECD는 투자위원회를 중심으로 각국 NCP들이 OECD 가이드라인을 효과적으로 이행하기 위한 일환으로 NCP 간 동료평가(Peer Review)를 강조하는 추세이다. 각 NCP로서는 동료평가가 자발적인 성격이라고는 하지만 부정적인 평가를 받게 되는 경우 국제적으로 많은 비난을 받을 소지가 크고 국가 이미지에도 심각한 타격을 입을 가능성이 높다. 더군다나 지난 2017년 3월, 기업책임경영에 관한 작업반(Working Party on Responsible Business Conduct) 회의에서 한국 NCP에 대하여 2019년 동료평가를 시행하기로 결정하였다. 이에 본 논문에서는 NCP 동료학습의 의의와 현황, 최근 시행되었던 덴마크와 벨기에 동료평가 사례들을 검토함으로써 우리 정부가 2019년 동료평가에 어떻게 대비해야 하는지 정책적 제언을 하고 있다.

• 한국정보교육학회:학술대회논문집
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• 한국정보교육학회 2021년도 학술논문집
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• pp.253-259
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• 2021

인공지능이 사회 전반에 퍼진 현대사회에 걸맞게 교육부는 2025년에 유치원과 초·중·고 수업에 AI교육을 도입하고 2021년부터 관련된 학습 자료와 교재를 개발하기로 하였다. 우리나라는 유치원 및 초등학교 저학년 학생들을 위한 국가 주도 AI 교육이 이루어지지 않고 있어 체계적인 교재가 없는 실정이다. 따라서, 본 연구는 정영식·임서은(2020)이 연구한 유치원 SW 교육과정인 GSP 교육과정을 토대로 패턴 분석 기반의 컴퓨팅 사고력 계발을 위한 교재를 설계하여 제시하였다. 교재 설계를 위해 수업 절차를 도입 활동(스토리 도입, 놀이1), 전개 활동(놀이2~놀이4), 정리활동(정리, 생각 더하기)로 순차적으로 분류하였다. 각 활동에 대한 설명과 함께 교재와 교구를 제시함으로써 보충 설명을 제시하였다. 본 연구가 2025년에 이루어질 AI 교육에 도움이 되기 위해서는 실제 적용을 통한 효과를 입증하는 연구가 뒤따라야 한다.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• Journal of Gastric Cancer
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• 제23권3호
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• pp.388-399
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• 2023

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

• 대한원격탐사학회지
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• 제40권1호
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.