• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.789-797
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• 2003

An implementation scheme and some improvements are proposed to adopt public-licensed operating system, Linux and de-facto world-wide network standard, TCP/IP into the field of behavior-based autonomous mobile robots. To demonstrate the needs of scheme and the improvement, an analysis is performed on a server/client communication problem with real time Linux previously proposed, and another analysis is also performed on interactions among TCP/IP communications and the performance of Linux system using them. Implementation of behavior-based control architecture on real time Linux is proposed firstly. Revised task-scheduling schemes are proposed that can enhance the performance of server/client communication among local tasks on a Linux platform. A new method of TCP/IP packet flow handling is proposed that prioritizes TCP/IP software interrupts with aperiodic server mechanism as well. To evaluate the implementation scheme and the proposed improvements, performance enhancements are shown through some simulations.

• Convergence Security Journal
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• v.22 no.2
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• pp.3-8
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• 2022

Security threats occur from the outside, but more often from the inside. In particular, since the internal user knows about the information service, the security threat damage caused by the internal user is greater. In this environment, the actions of all users accessing information services should be monitored and recorded in real-time. However, the current operating system records only the logs of system and application execution, so there is a limit to monitoring user behavior in real-time. In such a security environment, damage may occur due to user's unauthorized actions. To solve this problem, this study proposes an architecture that monitors user behavior in real-time in a Linux environment. As a result of verifying the function to confirm the effectiveness of the proposed architecture, the console input values and output angles of all users who have access to the operating system are monitored in real-time and stored. Although the performance of the proposed architecture is somewhat slower than the identification and authentication functions provided by the operating system, it was confirmed that the performance was not at a level that users would recognize, and thus it was judged to be sufficiently effective. However, since this study focuses on monitoring the console behavior, it is impossible to monitor the behavior of user applications running in the background, so additional research is needed.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.149-151
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• 2001

In real-time control system, it is essential to confirm the timing behavior of all tasks because these tasks of real-time controller have to finish their processes within the specified time intervals called a deadline. In order to satisfy this objective, the timing analysis of a real-time system such as a schedulability test must be performed during the system design phase. This paper presents a Matlab toolbox for simulation of real-time control system based on OSEK-OS, which is one of the most widely adopted real-time operating systems in automotive industry. The toolbox allows the user to explore the timely behavior of control algorithms, and to study the interaction between the object of the OSEK-OS, such as task, scheduler and resource etc.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Journal of Korea Foundry Society
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• v.30 no.3
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• pp.100-110
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• 2010

The solidification sequence and formation of intermetallic phase of Fe-rich Al-Si-Cu alloy were investigated by using real-time imaging of synchrotron X-ray radiation. Effects of cooling rate during uni-directional solidification on the resultant solidification behavior was also studied in a specially constructed vacuum chamber in the SPring-8 facility. The series of radiographic images were complementarily analyzed with conventional analysis of OM and SEM/EDX for phase identification. Detailed solidification sequence and formation mechanisms of various phases were discussed based on real-time image analysis. The growth rates of $\alpha$-AlFeMnSi and ${\beta}-Al_5FeSi$ were measured in order to understand the growth behavior of each phase. It is suggested that real-time imaging technique can be a powerful tool for the precise understanding of solidification behavior of various industrial materials.

• Structural Engineering and Mechanics
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• v.16 no.6
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• pp.643-654
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• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.55-62
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• 2015

In this paper, we proposed the method that enabling warning through real-time analysis of dangerous driving behavior, improving driving habits and safe driving using the digital tachograph. Most of traffic accidents and green drive are closely related of driving habits. These wrong driving habits need to be improved by the real-time analysis, warning and automated method of driving habits. We confirmed the proposed that the method will help support eco-driving, safe driving through real-time analysis of driving behavior and warning through the method implementation and experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Review of KIISC
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• v.5 no.2
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• pp.32-48
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• 1995

Our paper describes an Intrusion Detection Parallel System(IDPS) which detects an anomaly activity corresponding to the actions that interaction between near detection events. IDES uses parallel inductive approaches regarding the problem of real-time anomaly behavior detection on rule-based system. This approach uses sequential rule that describes user's behavior and characteristics dependent on time. and that audits user's activities by using rule base as data base to store user's behavior pattern. When user's activity deviates significantly from expected behavior described in rule base. anomaly behaviors are recorded. Observed behavior is flagged as a potential intrusion if it deviates significantly from the expected behavior or if it triggers a rule in the parallel inductive system.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.187-193
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• 2015

When developing a vehicle control system, simulation methods are widely used to validate the whole system in the early development phase. With this regard, the simulator should correctly behave just like the real parts that are not yet implemented while interacting with already implemented parts in real-time. However, most simulators cannot provide functionally and temporally accurate behaviors of the target system. In order to overcome this limitation, this paper proposes a novel real-time simulation technique that can efficiently simulate the temporal behavior as well as the functional behavior of the simulation target system.