• Bulletin of the Korean Chemical Society
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• v.10 no.4
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• pp.372-374
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• 1989

The new viscosity theory is applied to the abnormal behavior of the viscosity near the critical point. This theory suggests that the viscosity is equal to the product of the absolute pressure(kinetic pressure + internal pressure) and the collision time. We can find this abnormal behavior to be due to the large collision time near the critical point. The agreements between theoriticals and experimentals of the critical enhancement are satisfactory.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.125-137
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• 2012

As the Internet use explodes recently, the malicious attacks and hacking for a system connected to network occur frequently. For such reason, lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• International Commerce and Information Review
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• v.9 no.3
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.

• Journal of the Korean Ceramic Society
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• v.32 no.5
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• pp.587-593
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• 1995

Abnomal grain growth behavior of BaTiO3 ceramics was investigated with addition of seed grains. It was foudn that the nucleation rate of abnormal grain was constant and growth of abnormal grain was linearly increased with sitnering time, regardless of amount of seed grains. These facts were also confirmed by fitting of the volume fraction of abnormal grain vs. sintering time using Avrami type equation (n=4). It was suggested that seed grains did not change the nucleation rate or growth mechanism of abnormal grain but increase the number of abnormal grains at initial stage of sintering and then it led to fine microstructure of BaTiO3 ceramics.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.438-438
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• 2010

The abnormal behavior of the argon metastable density during the E-H mode transition in argon ICP discharge was investigated. Lots of investigations including global models expected that during and after the mode transition of ICP discharge, the density of metastable increases with applied rf power (i.e. electron density). However, recent direct measurement of metastable density revealed that the metastable density of argon decreases with the applied power during and after the mode transition. This result may not be explained by the previous global model which is based on the assumption of the Maxwellian electron energy distribution function (EEDF). In this paper, to explain this abnormal behavior with simple manners, a simple global model taking account of the effect of the non-Maxwellian EEDFs incorporating into a set of coupled rate equations is proposed. The result showed that the calculated metastable density taking account of non-Maxwellian EEDF and its evolution during the transition has an abnormal behavior with electron density and is in good agreement with the previous measurement results, indicating the close coupling of electron kinetics and the behavior of metastable density. The proposed simple model is expected to provide qualitative kinetic insight to understand the behavior of the metastable density in various plasma discharges which typically exhibit non-Maxwellian distribution.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.8
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• pp.731-737
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• 2014

This paper presents abnormal behavior detection in crowd within surveillance video. We have defined below two cases as a abnormal behavior; first as a sporadically spread phenomenon and second as a sudden running in same direction. In order to detect these two abnormal behaviors, we first extract the motion vector and propose a new descriptor which is combined MHOF(Multi-scale Histogram of Optical Flow) and DCHOF(Directional Change Histogram of Optical Flow). Also, binary classifier SVM(Support Vector Machine) is used for detection. The accuracy of the proposed algorithm is evaluated by both UMN and PETS 2009 dataset and comparisons with the state-of-the-art method validate the advantages of our algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.111-121
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• 2011

Intelligent video analysis systems require techniques which can predict accidents and provide alarms to the monitoring personnel. In this paper, we present an abnormal behavior analysis technique based on adaptive background generation. More specifically, abnormal behaviors include fence climbing, abandoned objects, fainting persons, and loitering persons. The proposed video analysis system consists of (i) background generation and (ii) abnormal behavior analysis modules. For robust background generation, the proposed system updates static regions by detecting motion changes at each frame. In addition, noise and shadow removal steps are also were added to improve the accuracy of the object detection. The abnormal behavior analysis module extracts object information, such as centroid, silhouette, size, and trajectory. As the result of the behavior analysis function objects' behavior is configured and analyzed based on the a priori specified scenarios, such as fence climbing, abandoning objects, fainting, and loitering. In the experimental results, the proposed system was able to detect the moving object and analyze the abnormal behavior in complex environments.

• Korean Journal of Psychosomatic Medicine
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• v.4 no.1
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• pp.138-145
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• 1996

The behaviors associated with illness are different depend upon individuals even if Patients complaints same symptoms and have same disease. Understanding the patterns of those behaviors become one of the important elements in determining the diagnostic and treatment approaches and treatment compliance. The sick role plays a essencial part in abnormal illness behavior. The characteristics of abnormal illness behavior ran be applied to many parts in medicine. In case of the various kinds of functional disorders whose organic foundations are obscure and in applying the cognitive therapy, rehabilitation program and occupational therapy, the assessment and evaluation of the abnormal illness behvior is known to be beneficial. For improving the comprehensive psychiatric treatments which could be applied to the Koreans more effectively in patients with somatoform disorders and other various kinds of neurotic disorders further researches especially on the medico-historical and socio-cultural aspects of the illness behavior should be followed. And understanding the abnormal illness behavior would be helpful in enhancing the medical cost effectiveness.

• Journal of the Korean Ceramic Society
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• v.32 no.1
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• pp.51-56
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• 1995

Variation of sintered density of BaTiO3 powder calcined at 120$0^{\circ}C$ and 135$0^{\circ}C$ was investigated with respect to the grain growth behavior. It was found that BaTiO3 powder, which was calcined at 120$0^{\circ}C$, showed abnormal grain growth behavior during sintering process. At initial stage of sintering process, the densification rate of specimen was accelerated with rapid grain growth caused by the abnormal grain growth. But with the increase of sintering time, abnormally grown grain met each other and the density of specimen decreased drastically due to coalescence of pores located in triple junction. On the contrary, BaTiO3 powder calcined at 135$0^{\circ}C$ showed normal grain growth behavior and gradually densified with the increase of sintering time.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.