• Journal of Institute of Control, Robotics and Systems
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• v.16 no.5
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• pp.510-516
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• 2010

In this paper, a dynamic GTS allocation method of wireless control networks is proposed for the use of factory automation using IEEE 802.15.4 MAC protocol. A superframe of IEEE 802.15.4 is applied to the transmission method of real-time periodic I/O data of wireless control systems within the limited time in factory environment. The method is proposed for efficient transmission of real-time periodic I/O traffic. The simulation results show the average network utilization and available I/O node numbers could be increased by the proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05c
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• pp.194-199
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• 2003

Depth error correction effect for maladjusted stereo cameras with calibrated pixel distance parameter is presented. The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of them request much memory space and time. Therefore the most reliable neural-network algorithm is derived for real-time matching of objects, which is composed of a dynamic programming algorithm based on sequence matching techniques.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.38-45
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• 2017

Attacking of computer and network is increasing as information processing technology heavily depends on computer and network. To prevent the attack of system and network, host and network based intrusion detection system has developed. But previous rule based system has a lot of difficulties. For this reason demand for developing a intrusion detection system which detects and cope with the attack of system and network resource in real time. In this paper we develop a real time intrusion detection system which is combination of APEX and LS-SVM classifier. Proposed system is for nonlinear data and guarantees convergence. While real time processing system has its advantages, such as memory efficiency and allowing a new training data, it also has its disadvantages of inaccuracy compared to batch way. Therefore proposed real time intrusion detection system shows similar performance in accuracy compared to batch way intrusion detection system, it can be deployed on a commercial scale.

• Journal of KIISE:Information Networking
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• v.27 no.2
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• pp.197-205
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• 2000

This paper proposes and evaluates a scheme which enhances real-time performance on the Ethernet-based residential network where cable length is very short and most of traffic passes through a specific device. The scheme aims at enhancing deadline meet ratio of the real-time traffic, for example, video stream by means of applying the functionalities such as queue discrimination and early packet discard as well as maintaining network load properly. The design includes the description on hardware interfaces along with the software modules such as scheduler, device driver and connection manager. The simulation results via SMPL suggest that the proposed network can be used as a cost-effective residential network. We also measure the permissible bandwidth for non-real-time traffic without degrading the performance of real-time traffic.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1493-1499
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• 2007

Real-time applications in a wireless sensor network environment require real-time transmissions from sensing nodes to sink nodes. Existing congestion control mechanisms have treated congestion problems in sensor networks, but they only adjust the reporting frequency or the sending rate in intermediate nodes. They were not suitable for real-time applications from the transmission delays point of view. In this paper, we suggest a new mechanism that can reduce the transmission delay and can increase the throughput for real-time applications in sensor network. This mechanism classifies data on the real-time characteristics, processes the data maintaining the real-time characteristics prior to the other data such as the non real-time data or the data lost the real-time characteristics. A modified frame format is also proposed in order to apply the mechanism to IEEE 802.15.4 MAC layer. The simulation based on ns-2 is accomplished in order to verify the performance of the suggested scheme from transmission delay and throughput standpoints. The simulation results show that the proposed algorithm has a better performance specifically when It applies to the real-time applications in sensor networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.7
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• pp.603-611
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• 2013

In this paper, we propose a new frame structure and the underlying dynamic resource control mechanism to support an integrated service, including a real-time (RT) service that requires to serve the end-to-end delay performance, as well as non-real-time (NRT) service, using Self-Organizing Time Division Multiple Access (SO-TDMA)-based MAC protocol in a multi-hop ad-hoc maritime communication network. The underlying frame structure is dynamically configured by resource allocation to guarantee the average target outage performance of the real-time service. Toward this end, we analyze the average outage probability and its performance is verified for the proposed frame structure by simulation.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.155-162
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• 2003

We propose a new technique far real-tine controller design of a autonomous cruise mobile robot with three drive wheels. The proposed control scheme uses a Caussian function as a unit function in the fuzzy neural network. and a back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-foray. The control performance of the proposed controller is illustrated by performing the computer simulation for trajectory tracking of the speed and azimuth of a autonomous cruise mobile robot driven by three independent wheels.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.127-136
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• 2023

Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.186-193
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• 2008

The Internet's low cost and ubiquity present an attractive option for real-time distributed control of processes on the factory floor. When integrated with the Internet, the LonWorks open control network can give ubiquitous accessibility with the distributed control nature of information on the factory floor. One of the most important points in real-time distributed control of processes is timely response. There are many processes on the factory floor that require timely response. However, the uncertain time delay inherent in the network makes it difficult to guarantee timely response in many cases. Especially, the transmission characteristics of the LonWorks/IP network show a highly stochastic nature. Therefore, the time delay problem has to be resolved to achieve high performance and quality of the real-time distributed control of the process in the LonWorks/IP Virtual Device Network (VDN). It should be properly predicted and compensated. In this paper, a new distributed control scheme that can compensate for the effects of the time delay in the network is proposed. It is based on the PID controller augmented with the Smith predictor and disturbance observer. Designing methods for output feedback filter and disturbance observer are also proposed. Tracking position control experiment of a geared DC Servo motor is performed using the proposed control method. The performance of the proposed controller is compared with that of the Internal Model Controller (IMC) with the Smith predictor. The result shows that the performance is improved and guaranteed by augmenting a PID controller with both the Smith predictor and disturbance observer under the stochastic time delay in the LonWorks/IP VDN.