• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.637-640
• /
• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• Elastomers and Composites
• /
• v.51 no.3
• /
• pp.206-211
• /
• 2016

Experimental error sources for thermogravimetric analysis (TGA) of poly(ethylene-co-vinyl acetate) (EVA) were investigated and sample preparation method to reduce the experimental error was suggested. Maximum dissociation temperatures of EVA for the first and second dissociation reactions ($T_{m1}$ and $T_{m2}$, respectively) were measured. By decreasing the weight of raw EVA, the $T_{m1}$ increased but the the $T_{m2}$ decreased. When weight of the raw EVA was over 10 mg, the TGA curve showed abnormal behaviors. The abnormal TG behaviors were explained by gathering and instantaneous evaporation of acetic acid formed by deacetylation of the VA unit. When TGA analysis of EVA was performed using untreated (raw) EVA, the experimental errors were about 1%. In order to eliminate the abnormal TG behaviors and to reduce the experimental errors, EVA film made by solvent casting was used. For the treated EVA (EVA film), the abnormal TG behaviors did not appear, the $T_{m1}$ decreased by about $2^{\circ}C$ but the $T_{m2}$ increased by about $6^{\circ}C$, and the experimental errors were reduced by 0.5%.

• Journal of Information Processing Systems
• /
• v.16 no.3
• /
• pp.612-628
• /
• 2020

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects' behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.

• Journal of the Korean Ceramic Society
• /
• v.36 no.12
• /
• pp.1327-1334
• /
• 1999

Behaviors of abnormal expansion during calcination process of Ba2Ti9O20 ceramics and its related effects on the sintering characteristics were investigated as a function of precursors. When BaCO3 and TiO2 powders were used as starting materials. BaTi4O9 phase which has relatively large molar volume was formed drastically with abnormal ex-pansion during the calcination at 95$0^{\circ}C$ to 115$0^{\circ}C$ ON the contrary using BaTiO3 and TiO2 powders as starting materials led to retardation of the formation of BaTi4O9 phase and concurrently suppressed the abnormal expansion during cal-cination process. Especially the calcined powder of BaTiO3 and TiO2 had advantages in the densification and formation of Ba2Ti9O20 single phase in the sintering process.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.157-163
• /
• 2023

The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through end-users. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.39 no.1
• /
• pp.1-12
• /
• 2019

This study defined abnormal behaviors such as bending deformations or buckling behaviors occurred in high strength steel pipe strut system, and carried out a full-scale bending test for different connection types. A parametric study was carried out to gain an insight about structural performances considering abnormal behavior effects in high strength steel pipe strut system. Five abnormal behaviors were considered as undesirable deflections of strut structures, which are basic load combination, excessive excavation situations, impact loading effects, additional overburden loads, load combinations, and strut lengths. Subsequent simulation results present various influences of parameters on structural performances of the strut system. Based on the results, we propose methods to prevent unusual behaviors of pipe-type strut structures made of high strength steels.

• Journal of Digital Contents Society
• /
• v.14 no.4
• /
• pp.589-596
• /
• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Annual Conference of KIPS
• /
• 2019.05a
• /
• pp.542-544
• /
• 2019

Changes in a person's health affect one's lifestyle and work activities. According to the World Health Organization (WHO), abnormal activity is growing faster in people aged 60 or more than any other age group in almost every country. This trend steadily continues and expected to increase further in the near future. Abnormal activity put these people at high risk of expected incidents since most of these people live alone. Human abnormal activity analysis is a challenging, useful and interesting problem among the researchers and its particularly crucial task in life and health care areas. In this paper, we discuss the problem of abnormal activities of old people lives alone at home. We propose Convolutional Neural Network (CNN) based model to detect the abnormal behaviors of elderlies by utilizing six simulated action data from daily life actions.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.115-119
• /
• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Women's Health Nursing
• /
• v.19 no.1
• /
• pp.57-65
• /
• 2013

Purpose: This study identified the effects of body weight control behaviors on bone mineral density (BMD) in Korean women aged 20 to 39 years. Methods: A secondary analysis of the 5th Korean National Health and Nutrition Examination Survey was conducted. Asian-Pacific criteria of BMI (Body Mass Index) and BMD were calculated for 1,026 women selected. The effects of body weight control behaviors were assessed using binary multiple logistic regression analysis while controlling for BMI. Results: Osteopenia and osteoporosis rates were 32.8% and 2.0%, respectively. About 69% of women performed weight control behaviors, and a combination of diet/exercise (22.7%) and drug added methods (10.9%) for weight control. Women who performed both diet control and exercise had a lower possibility to have abnormal BMD than those who did not try weight control (OR=0.67, CI=0.45~0.98, p=.039). Further weight control behaviors did not influence abnormal BMD. Conclusion: Body weight control should include proper diet and exercise in accordance with each woman's BMI level.