• Journal of Powder Materials
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• v.13 no.2 s.55
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• pp.119-123
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• 2006

The grain growth behavior of $0.95Na_{1/2}Bi_{1/2}TiO_{3}-0.05BaTiO_{3}$ (NBT-5BT) has been investigated with respect to the grain shape. The powder compacts of NBT-5BT were sintered at 1200 for various times. The corresponding equilibrium shape was a round-edged cube with flat {100}-faces. Abnormal grains were not observed in the specimens sintered for 1 to 12 h but abnormal grains appeared when sintered for 24 h. Before the formation of abnormal grains, a valley was observed in the measured grain size distribution of NBT-5BT, showing that the grain size distribution was a combination of two unimodal distributions. The present result suggests that the grain growth in NBT-5BT was governed by the growth of facet planes which would occur via 2-dimansional nucleation and growth.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.52-60
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• 2024

APT (Advanced Persistent Threat) attack is a dangerous, targeted attack form with clear targets. APT attack campaigns have huge consequences. Therefore, the problem of researching and developing the APT attack detection solution is very urgent and necessary nowadays. On the other hand, no matter how advanced the APT attack, it has clear processes and lifecycles. Taking advantage of this point, security experts recommend that could develop APT attack detection solutions for each of their life cycles and processes. In APT attacks, hackers often use phishing techniques to perform attacks and steal data. If this attack and phishing phase is detected, the entire APT attack campaign will be crash. Therefore, it is necessary to research and deploy technology and solutions that could detect early the APT attack when it is in the stages of attacking and stealing data. This paper proposes an APT attack detection framework based on the Network traffic analysis technique using open-source tools and deep learning models. This research focuses on analyzing Network traffic into different components, then finds ways to extract abnormal behaviors on those components, and finally uses deep learning algorithms to classify Network traffic based on the extracted abnormal behaviors. The abnormal behavior analysis process is presented in detail in section III.A of the paper. The APT attack detection method based on Network traffic is presented in section III.B of this paper. Finally, the experimental process of the proposal is performed in section IV of the paper.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.616-619
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• 2009

Color tracking behavior of in the fringe-field switching (FFS) mode using a liquid crystal with positive dielectric anisotropy has been studied. In the in-plane switching and vertical alignment devices, color chromaticity at normal direction changes from bluish to yellowish white linearly with increasing grey levels from dark to white state. Interestingly, abnormal behavior in color tracking is observed in FFS devices using a liquid crystal with positive dielectric anisotropy, that is, it changes from bluish to yellowish up to a certain middle grey level but turns over to bluish white with further increasing from a grey level to a fully white state. In this paper, we analyze this abnormal effect from the calculated and experimental results.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1519-1530
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• 2020

As livestock farms grow in size, the number of breeding individuals increases, making it difficult to manage livestock. Livestock farms require an integrated management system such as a monitoring system, an access control system, and an abnormal behavior detection system to manage livestock houses. In this paper, a smart korean cattle livestock management system using IoT and AI technology was proposed for livestock management in livestock farms. The smart korean cattle farm management system consists of a monitoring and control system, a vehicle access management system, and an abnormal cattle behavior detection system. It is expected that this will help manage large-scale livestock houses, and additional research is needed to improve the performance of abnormal behavior detection in the future.

• Sleep Medicine and Psychophysiology
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• v.11 no.1
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• pp.37-43
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• 2004

Objective: The purpose of this paper is to study the possible differences in clinical and polysomnographic findings, depending on the presence or absence of subjective complaints of abnormal sleep behavior, in patients with RWA on polysomnography. Method: We reviewed patient records and polysomnographic data of patients referred to the Sleep Laboratory at Seoul National University Hospital from June 1996 through October 2002. We defined the manifest RBD group (n=32) as patients having both complaints of abnormal sleep behavior and RWA on polysomnography. The latent RBD group (n=20) consisted of patients who exhibited RWA on polysomnography but did not complain of abnormal sleep behavior. The clinical characteristics and polysomnographic findings between the two groups were compared and analyzed. Results: Fifty-two subjects had RWA, as detected by polysomnography (42 males and 10 females, mean age of $55.1{\pm}19.1\;years$). Subjects in the manifest RBD group were significantly older than those in the latent RBD group ($61.59{\pm}13.5$ vs. $44.70{\pm}2.76\;years$, independent t-test, p<0.01). More subjects in the manifest RBD group exhibited abnormal REM behavior on polysomnography than did subjects in the latent RBD group (81.3 vs. 50.0%, Fisher's exact test, p<0.05). No significant differences between the groups were found in the prevalence of brain disorders and primary sleep disorders, gender proportion, and sleep architecture. Conclusion: No difference in sleep architecture was found between the manifest and the latent RBD groups. Only age and the presence of abnormal sleep behavior on polysomnography differentiated the two groups. We suggest that RWA on polysomnography without complaints of abnormal sleep behavior may be early manifestation of manifest RBD. Attention to RWA on polysomnography is necessary to help prevent full-blown RBD from developing.

• Journal of the Korean Society for Heat Treatment
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• v.11 no.4
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• pp.258-267
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• 1998

For the purpose of investigating the growth characteristics and composition of nitrides, gas nitridings of the austenitic stainless steel, STR 36 heat resisting steel and martensitic stainless steel are investigated at the temperature ranges between $500^{\circ}C$ and $675^{\circ}C$ for 5hours under the $75%NH_3+5%CO_2+20%$Air gas atmosphere. When gas nitriding the austentic stainless steel and STR 36 heat resisting alloy, the abnormal growth behavior of compound layer deviating from the conventional diffusion law with increasing temperature appears, while the compound layer of martensitic stainless steel shows the normal diffusional growth behavior. From the examination of microstructure, X-ray diffraction and hardness test, it is concluded that the abnormal growth behavior of compound layer with increasing temperature induces from the formation and dissolution of CrN and ${\gamma}^{\prime}-Fe_4N$ at the nitriding temperature ranges of $600{\sim}650^{\circ}C$.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.891-892
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• 2006

To test the correlation between grain shape and growth behavior we prepared WC-TiC-Co samples with rounded (Ti, W)C grains and faceted WC grains. The growth of rounded (Ti, W)C grains was normal. In contrast, the growth of faceted WC grains was abnormal or suppressed depending on the initial size of WC particles. These observations were explained using growth theories of crystals in a liquid and were also confirmed by a simulation using their growth equations. The present results thus demonstrate that the growth behavior of carbide grains in a liquid is governed only by their shape, irrespective of the presence of another phase.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.463-468
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• 2010

본 논문은 본 연구팀이 행동패턴 분석을 위하여 개발한 BPP(Behavior Pattern Prediction)알고리즘의 가중치(weight) 속성을 객관적으로 수식화 하는 방법과 가중치와 행동 프로파일을 이용하여 정상/비정상 행동여부를 판단하는 ABA(Abnormal Behavior Analysis) 알고리즘을 제안한다. 가중치는 거주자의 방과 행동 사이의 연관성을 나타내며 가중치가 제한된 범위 내에서 증가 할수록 행동에 대한 관심이 크다. 구축한 사용자 프로파일의 주요 구성 요소로는 행동이 지속된 시간 과 행동 발생 횟수이다. ABA 알고리즘은 가중치와 행동 발생 횟수, 행동 지속시간과의 상관분석 결과를 참조 하였으며, 이산 가중치 데이터를 분석하여 비정상적인 행동을 탐지한다.

• Journal of Powder Materials
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• v.29 no.2
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• pp.110-117
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• 2022

The grain growth behavior in the (1-x)K_0.5Na_0.5NbO₃-xCaZrO₃ (KNNCZ-x) system is studied as a function of the amount of CZ and grain shape. The (1-x)K_0.5Na_0.5NbO₃-xCaZrO₃ (KNNCZ-x) powders are synthesized using a conventional solid-state reaction method. A single orthorhombic phase is observed at x = 0 - 0.03. However, rhombohedral and orthorhombic phases are observed at x = 0.05. The grain growth behavior changes from abnormal grain growth to the suppression of grain growth as the amount of CaZrO₃ (CZ) increases. With increasing CZ content, grains become more faceted, and the step-free energy increases. Therefore, the critical growth driving force increases. The grain size distribution broadens with increasing sintering time in KNNCZ-0.05. As a result, some large grains with a driving force larger than the critical driving force for growth exhibit abnormal grain growth behavior during sintering. Therefore, CZ changes the grain growth behavior and microstructure of KNN. Grain growth at the faceted interface of the KNNCZ system occurs via two-dimensional nucleation and growth.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.11-19
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• 2021

An abnormal object refers to a person, an object, or a mechanical device that performs abnormal and unusual behavior and needs observation or supervision. In order to detect this through artificial intelligence algorithm without continuous human intervention, a method of observing the specificity of temporal features using optical flow technique is widely used. In this study, an abnormal situation is identified by learning an algorithm that translates an input image frame to an optical flow image using a Generative Adversarial Network (GAN). In particular, we propose a technique that improves the pre-processing process to exclude unnecessary outliers and the post-processing process to increase the accuracy of identification in the test dataset after learning to improve the performance of the model's abnormal behavior identification. UCSD Pedestrian and UMN Unusual Crowd Activity were used as training datasets to detect abnormal behavior. For the proposed method, the frame-level AUC 0.9450 and EER 0.1317 were shown in the UCSD Ped2 dataset, which shows performance improvement compared to the models in the previous studies.