• Journal of KIISE
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• v.41 no.12
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• pp.1152-1159
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• 2014

In this paper, we presents a system to ensure the safety of residents through appropriate action or alarm in case the residents occurs an emergency situation and abnormal activity. We collect and analysis real-time data of living environment of the residents using video and sensor. The existing system have been determined by using only the sensor data it have several problems. Our system attach camera to solve the existing system problem. We use weighted difference image and motion vector. The existing system, it takes about 48 hours to determine that an abnormal activity occurs. However, our system takes less than 1 hour.

• 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 Korean Data and Information Science Society
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• v.27 no.1
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• pp.1-8
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• 2016

In the recently, violent crime and accidental crime has been generated continuously. Consequently, people anxiety has been heightened. The Closed Circuit Television (CCTV) has been used to ensure the security and evidence for the crimes. However, the video captured from CCTV has being used in the post-processing to apply to the evidence. In this paper, we propose a flexible multi-level models for estimating whether dangerous behavior and the environment and context for pedestrians. The situation analysis builds the knowledge for the pedestrians tracking. Finally, the decision step decides and notifies the threat situation when the behavior observed object is determined to abnormal behavior. Thereby, tracking the behavior of objects in a multi-region, it can be seen that the risk of the object behavior. It can be predicted by the behavior prediction of crime.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• Earthquakes and Structures
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• v.20 no.4
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• pp.417-430
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• 2021

Due to functional requirements, SRC column-RC beam abnormal joints with characteristics of strong beam weak column, variable column section, unequal beam height and staggered height exist in the Steel reinforced concrete (SRC) frame-bent main building structure of thermal power plant (TPP). This paper presents the experimental results of these abnormal joints through cyclic loading tests on five specimens with scaling factor of 1/5. The staggered height and whether adding H-shaped steel in beam or not were changing parameters of specimens. The failure patterns, bearing capacity, energy dissipation and ductile performance were analyzed. In addition, the stress mechanism of the abnormal joint was discussed based on the diagonal strut model. The research results showed that the abnormal exterior joints occurred shear failure and column end hinge flexural failure; reducing beam height through adding H-shaped steel in the beam of abnormal exterior joint could improve the crack resistance and ductility; the abnormal interior joints with different staggered heights occurred column ends flexural failure; the joint with larger staggered height had the higher bearing capacity and stiffness, but lower ductility. The concrete compression strut mechanism is still applicable to the abnormal joints in TPP, but it is affected by the abnormal characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.1-12
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• 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 Korean Society of Steel Construction
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• v.10 no.3 s.36
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• pp.383-391
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• 1998

Launching truss used for constructing the precast segmental concrete bridge has upper chord, lower chord and diagonal members. And the pin is used for connecting these members. From the field loading test carried out for investigating the actual behavior of launching truss, the great difference is analyzed between measured stress and calculated stress. Based on measured value, the structural analysis are carried out about assumed abnormal behavior of connection part. From the results of analysis, it is analyzed that the abnormal behavior of connection part greatly affect the structural behavior of launching truss. In addition, from the investigation of safety of launching truss, it is evaluated that the launching truss has enough safety with normal behavior of connection part.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.43-50
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• 2019

With the development of IT technology, computer-related crimes are rapidly increasing, and in recent years, the damage to ransomware infections is increasing rapidly at home and abroad. Conventional security solutions are not sufficient to prevent ransomware infections, and to prevent threats such as malware and ransomware that are evolving, a combination of deep learning technologies is needed to detect abnormal behavior and abnormal symptoms. In this paper, a method is proposed to detect user abnormal behavior using CNN-LSTM model and various deep learning models. Among the proposed models, CNN-LSTM model detects user abnormal behavior with 99% accuracy.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.