• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.657-659
• /
• 2004

CAN is a serial communication bus for real-time controls and automations in distributed control systems. In distributed control systems, occasionally a sensor module and a controller are not in the same node and physically separated. In order for the signal from a sensor node to reach the controller node, the signal must travel through network. CAN has a certain capabilities to deal with real-time data. However, when many nodes on the networks try to send data on the same network, the arbitration mechanism to solve the data collision problem is necessary. This situation causes the time delay which has detrimental effects on the performance of the control systems. This paper proposes a method to solve the problem due to the time delay in distributed control system using CAN. Time delay is approximated to an element with a rational transfer function using Pade approximation and Mu~synthesis method is applied. Since time delay in the network is not constant, the time delay element is considered to be an uncertainty block with a bound. The proposed method is applied to the experimental system with CAN and proved to be effective.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.5
• /
• pp.998-1013
• /
• 2013

Wireless wearable sensor networks have emerged as a promising technique for human motion tracking due to the flexibility and scalability. In such system several wireless sensor nodes being attached to human limb construct a wearable sensor network, where each sensor node including MEMS sensors (such as 3-axis accelerometer, 3-axis magnetometer and 3-axis gyroscope) monitors the limb orientation and transmits these information to the base station for reconstruction via low-power wireless communication technique. Due to the energy constraint, the high fidelity requirement for real time rendering of human motion and tiny operating system embedded in each sensor node adds more challenges for the system implementation. In this paper, we discuss such challenges and experiences in detail during the implementation of such system with wireless wearable sensor network which includes COTS wireless sensor nodes (Imote 2) and uses TinyOS 1.x in each sensor node. Since our system uses the COTS sensor nodes and popular tiny operating system, it might be helpful for further exploration in such field.

• Speech Sciences
• /
• v.3
• /
• pp.119-131
• /
• 1998

We have developed a keyword spotting system for automatic speech editing. This system recognizes the only keyword 'MBC news' and then sends the time information to the host system. We adopted a vocabulary dependent model based on continuous hidden Markov model, and the Viterbi search was used for recognizing the keyword. In recognizing the keyword, the system uses a parallel network where HMM models are connected independently and back-tracking information for reducing false alarms and missing. We especially focused on implementing a stable and practical real-time system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.695-699
• /
• 2003

It is general to use the control network in control systems in order to reduce the complexity of the related wiring harnesses and to improve the system flexibility. CAN becomes one of the most popular network protocols because of its low price, multiple sources, high performance and reliability. This paper describes a CAN based real-time control of the fire detection system for the intelligent building system. The proposed fire detection and alarm system is stronger than the previous one against noises and communication media faults and can solve many problems such as complex cabling and increment of I/O ports by using many sensors. Furthermore, MMI can be achieved easily with the personal computer that is used for replacing the traditional monitoring system. The proposed system is implemented and the experimental results are given.

• Journal of Advanced Navigation Technology
• /
• v.17 no.6
• /
• pp.664-671
• /
• 2013

In this paper, in an aircraft power can be measured by wireless AEMS (aircraft electric power measurement monitoring system) system is proposed. AEMS has been design based on current commercialized power measuring systems analysis with improvement and connects it with most talked about item, smart phone and monitoring system. And also adopting real time power measuring system, constitute more practical power measuring system by controlling electricity usage in real time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.9
• /
• pp.50-55
• /
• 2011

This research is to apply the adaptive control of neuron networks for the real-time attitude control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of attitude control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. In this research we used the response property curve of the robot instead of the activation function of neural network algorithms, so the adaptive control system of neural networks constructed without the information of modeling can perform a real-time control. The proposed adaptive control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.5
• /
• pp.969-975
• /
• 2002

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.76-85
• /
• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• IE interfaces
• /
• v.15 no.4
• /
• pp.374-381
• /
• 2002

In this paper, we design and implement a real-time route guidance system(RGS) in large-scale networks. Coupled with the well-known mathematical routing algorithms, we devise an RGS for knowledge aquisition and self-learning ability within the framework of the expert system. Through off-line construction of database, on-line treatment of unexpected traffic accidents, etc., the developed RGS can provide drivers with good quality real-time routing information. The practical effectiveness of the proposed system is demonstrated in terms of response time and route appropriateness.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.39 no.2
• /
• pp.199-206
• /
• 2015

In this study, a new diagnosis method which can predict the working states of a pipe or its element in realtime is proposed by using an artificial neural network. The displacement data of an inspection element of a piping system are obtained by the use of PIV (particle image velocimetry), and are used for teaching a neural network. The measurement system consists of a camera, a light source and a host computer in which the artificial neural network is installed. In order to validate the constructed monitoring system, performance test was attempted for two kinds of mobile phone of which vibration modes are known. Three values of acceleration (minimum, maximum, mean) were tested for teaching the neural network. It was verified that mean values were appropriate to be used for monitoring data. The constructed diagnosis system could monitor the operation condition of a gas pipe.