• Journal of the Korean Institute of Telematics and Electronics
• /
• v.26 no.10
• /
• pp.1576-1583
• /
• 1989

In this paper, we describe real-time optical implementation of the Hopfield neural network model for two-dimensional associative memory by using commercial LCTV and Multifocus For real-time processing capability, we use LCTV as a memory mask and a input spatial light modulator. Inner product between input pattern and memory matrix is processed by the multifocus holographic lens. The output signal is then electrically thresholded fed back to the system input by 2-D CCD camera. From the good experimental results, the proposed system can be applied to pattern recognition and machine vision in future.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.68.4-68
• /
• 2001

In the area of factory automation (FA), a lot of researches are being executed about intelligent system using network. Conventional industry controllers are implemented the connection with other controllers by using asynchronous serial communications. These methods of communication have an advantage of easy implementation but have disadvantages of lack of real-time characteristics needed in motion control, slow baud rate too low to exchange required data sufficiently and low integrity because of different communication protocol. Serial real-time communication system (SERCOS) provides international standard communication specifications (IEC 61491) for interfacing between control units and drives from different manufacturers, has real-time ...

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2001.04a
• /
• pp.239-243
• /
• 2001

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.04a
• /
• pp.171-175
• /
• 2000

Neural networks are in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.10a
• /
• pp.108-112
• /
• 2000

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD (an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Proceedings of the IEEK Conference
• /
• 2003.11c
• /
• pp.121-124
• /
• 2003

Passive optical networks (PONs) will be the pervasive choice in the design of next-generation access networks. One possible solution to implementing Passive optical access network is to rely on wavelength-division multiplexing (WDM). In this paper, we solve the problem of providing real-time service to both hard and soft real-time messages in conjunction with a conventional vest-effort service in WDM. we propose an adaptive scheduling algorithm to schedule and manage the message transmissions in the optical access network. Proposed algorithm is MLF-MQF. Also, we suggest dynamic bandwidth algorithm(DBA) in Loop-back WDM-PON (LPB-PON).our mechanism based on bandwidth reservation protocol.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2007.11a
• /
• pp.508-509
• /
• 2007

For the safty driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.8A
• /
• pp.1160-1168
• /
• 2000

This paper presents a real-time integrated traffic management (RITM) scheme that can effectively manage variable bit rate (VBR) and available bit rate (ABR) traffics having unpredictable characteristics in asynchronous transfer mode (ATM) networks. An unique feature of this scheme is that it has a special ATM cell control block which makes it possible to monitor bursty traffics in real-time so that the delay incurred to measure cell arrival rate is minimized. Additionally, the proposed scheme intends to dynamically reassign the leftover network resources to VBR/ABR connections without any deterioration in quality of service (QoS) of the existing connections. The RITM scheme has been verified to reliably monitor incoming traffics and to efficiently manage network resources by computer simulations. The capability of managing the incoming ATM traffics in real-time helps determine an optimal acceptable number of user connections for a given network condition. We can use this value as a threshold to protect the network from being congested and to find out a cost-effective buffer design method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.226-231
• /
• 2013

In The matching algorithm for automatic packaging of drugs is essential to determine whether the canister can exactly refill the suitable medicine. In this paper, we propose a hierarchical neural network with the upper and lower layers which can perform real-time processing and classification of many types of medicine bottles to prevent accidental medicine disaster. A few number of low-dimensional feature vector are extracted from the label images presenting medicine-bottle information. By using the extracted feature vectors, the lower layer of MLP(Multi-layer Perceptron) neural networks is learned. Then, the output of the learned middle layer of the MLP is used as the input to the upper layer of the MLP learning. The proposed hierarchical neural network shows good classification performance and real- time operation in the test of up to 30 degrees rotated to the left and right images of 100 different medicine bottles.

• Journal of the Korean GEO-environmental Society
• /
• v.19 no.1
• /
• pp.25-30
• /
• 2018

Conventional monitoring method is used for evaluation of the reservoir and levee at the highest height sections. In recent years, automated measurement technology has been developed, and the measurement results are transmitted, collected and stored in real time into management office. Despite the development of real time monitoring technology, the measurement results are not used directly or indirectly with facility management at real time. Recently, as wireless sensor network measurement technology has been developed based on internet of things, this study proposed a real - time measurement and evaluation system based on wireless sensor network technology in the reservoir structure. As a result of the seepage analysis for the application, it was confirmed that the volumetric water content changes together with the change of the seepage line inside the embankment body according to the change of the water level of the embankment. In other words, the applicability of the measurement system with the volumetric water ratio set as the sensor node was verified.