• Steel and Composite Structures
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• v.18 no.5
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• pp.1177-1195
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• 2015

Six specimens are tested to investigate the cyclic behavior of beam-to-column abnormal joints in steel moment-resisting frames, which are designed according to the principle of strong-member and weak-panel zone. Key parameters include the axial compression ratio of column and the section depth ratio of beams. Experimental results indicate that four types of failure patterns occurred during the loading process. The $P-{\Delta}$ hysteretic loops are stable and plentiful, but have different changing tendency at the positive and negative direction in the later of loading process due to mechanical behaviors of specimens. The ultimate strength tends to increase with the decrease of the section depth ratio of beams, but it is not apparent relationship to the axial compression ratio of column, which is less than 0.5. The top panel zone has good deformation capacity and the shear rotation can reach to 0.04 rad. The top panel zone and the bottom panel zone don't work as a whole. Based on the experimental results, the equation for shear strength of the abnormal joint panel zone is established by considering the restriction of the bottom panel zone to the top panel zone, which is suitable for the abnormal joint of H-shaped or box column and beams with different depths.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• 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.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2002.10a
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• pp.156-156
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• 2002

The goal of this study is to develop a biomarker used in monitoring abnormal behaviors of Japanese medaka (Oryzias latipes) as a model organism caused by hazardous chemicals that are toxic and persistent in the ecosystem. A widely used insecticide, diazinon (O, O-diethyl O- (2-isopropyl-4-methyl-6-pyrimidinyl) phosphorothioate), is highly neurotoxic to fish, and it is also well known that it causes vertebral malformation and behavioral changes of fish at relatively low concentrations. The fish behaviors were observed on a real time basis using an image processing and automatic data acquisition system. The genes potentially involved in the abnormal behaviors were cloned using suppression subtractive hybridization (SSH) technique. The untreated individuals showed common behavioral characteristics. When the test fish was affected by diazinon at a concentration of 0.1 and 1 ppm, some specific patterns were observed in its behavioral activity and locomotive tracks. The typical patterns were enhanced surfacing activity, opercular movement, erratic movement, tremors and convulsions as reported previously. The number of genes up-regulated tty diazinon treatment were 97 which includes 27 of unknown genes. The number of down-regulated genes were 99 including 60 of unknown genes. These gene expression patterns will be analyzed by the artificial neural networks such as self organization map (SOM) and multilayer perceptron (MLP), revealing the role of genes responsible for the behaviors. These results may provide molecular biological and neurobehavioral bases of a biomonitoring system for diazinon using a model organism such as fish.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• 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 Internet of Things and Convergence
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• v.5 no.2
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• pp.95-101
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• 2019

Entering the era of the 4^th Industrial Revolution and the Internet of Things, various services are growing rapidly, and various researches are actively underway. Among them, research on abnormal behaviors on various devices that are being used in the IoT is being conducted. In a hyper-connected society, the damage caused by one wrong device can have a serious impact on the various connected systems. In this paper, We propose a technique to cope with the problem that the threats caused by various abnormal behaviors such as anti-debugging scheme, anomalous process detection method and back door detection method on how to increase the safety of the device and how to use the device and service safely in such IoT environment.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.101-108
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• 2001

In this study, the static and dynamic characteristics of endmilling process were modelled and the analytic realization of chatter mechanism was discussed. In this reward, We have discussed on the comparative assessment of recursive time series modeling algorithms that cal represent time machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental works were performed to show the malfunctional behaviors. For this purpose, new recursive algorithm(RLSM) was adopted for the oil-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamics in regenerative chatter mechanics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.659-679
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• 2015

Distributed applications are composed of multiple nodes, which exchange information with individual nodes through message passing. Compared with traditional applications, distributed applications have more complex behavior patterns because a large number of interactions and concurrent behaviors exist among their distributed nodes. Thus, it is difficult to detect anomalous behaviors and determine the location and scope of abnormal nodes, and some attacks and misuse cannot be detected. To address this problem, we introduce a method for detecting anomalous behaviors based on process algebra. We specify the architecture of the behavior detection model and the detection algorithm. The anomalous behavior detection and analysis demonstrate that our method is a good discriminator between normal and anomalous behavior characteristics of distributed applications. Performance evaluation shows that the proposed method enhances efficiency without security degradation.

• Journal of Advanced Navigation Technology
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• v.17 no.2
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• pp.165-170
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• 2013

In this paper, we propose the information acquisition and analysis system for a vehicle driver in order to provide the safe driving environments. We first define the list of reckless driving behaviors and propose the recognition system, which recognizes the reckless behaviors, by using the acquired information. The collaboration among the information acquisition, the analysis, and the behavior comparison modules increases the accuracy of the recognition rate. Our system alarms to a vehicle driver in order to notify the potential to confront the dangerous situation due to the abnormal or reckless driving behaviors.