• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.62-70
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• 2003

This paper deals with a approximation and tracking control of hydraulic servo system using a real time recurrent neural networks (RTRN) with 2-dimensional iterative learning rule. And it was driven that 2-dimensional iterative learning rule in discrete time. In order to control the trajectory of position, two RTRN with same network architecture were used. Simulation results show that two RTRN using 2-D learning algorithm is able to approximate the plant output and desired trajectory to a very high degree of a accuracy respectively and the control algorithm using two same RTRN was very effective to control trajectory tracking of electro-hydraulic servo system.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1520-1524
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• 2000

TCP congestion control algorithm prevents network congestion through the control of outgoing traffic size. The network, therefore, should monitor the incoming traffic size of a TCP to determine whether or not a TCP follows standard congestion control algorithms. Some TCP friendly test algorithms are proposed, But, these algorithms cannot be used in real environments because a router in a network does not know the RTT of a TCP flow. In this study, we propose a new RTT determination algorithm that can be used in a router. Our proposed algorithms is validated through the simulation studies.

• Proceedings of the KIEE Conference
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• 2002.11c
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• pp.183-187
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• 2002

This paper proposes a remote control system that combines computer network and an autonomous mobile robot. We control remotely an autonomous mobile robot with vision via the internet to guide it under unknown environments in the real time. The main feature of this system is that local operators need a World Wide Web browser and a computer connected to the internet communication network and so they can command the robot in a remote location through our Home Page. This system offers an image compression method using motion H.263 concept which reduces large time delay that occurs in network during image transmission.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.45-45
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• 2000

This paper proposes a remote control system that combines computer network and an autonomous mobile robot. We control remotely an autonomous mobile robot with vision via the internet to guide it under unknown environments in the real time. The main feature of this system is that local operators need a World Wide Web browser and a computer connected to the internet communication network and so they can command the robot in a remote location through our Home Page. The hardware architecture of this system consists of an autonomous mobile robot, workstation, and local computers. The software architecture of this system includes the server part for communication between user and robot and the client part for the user interface and a robot control system. The server and client parts are developed using Java language which is suitable to internet application and supports multi-platform. Furthermore, this system offers an image compression method using motion JPEG concept which reduces large time delay that occurs in network during image transmission.

• Journal of Environmental Science International
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• v.6 no.3
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.768-770
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• 1996

This paper propose neural network which is one of self-organizing techniques. It is composed neural network controller as input signal is error and change of error which is optimal output, and is learned system by using a error back-propagation learning algorithm is one of error mimizing learning methods. In order to achieve practical real time control reduce on learning time, it is applied to load-frequency control of nonlinear power system with using a moment learning method. It is described in such a case considering constraints for a rate of increace generation-rate.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.227-237
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• 2019

Ultra-low latency control is one of the characteristics of 5G cellular network services, which means that the control loop is handled in milliseconds. To achieve this, it is necessary to identify time delay factors that occur in all components related to CPS control loop, including new 5G cellular network elements such as MEC, and to optimize CPS control loop in real time. In this paper, a novel CPS architecture for ultra-low latency control of CPS is designed. We first define the ultra-low latency characteristics of CPS and the CPS concept model, and then propose the design of the control loop performance monitor (CLPM) to manage the timing information of CPS control loop. Finally, a case study of MEC-based implementation of ultra-low latency CPS reviews the feasibility of future applications.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.676-684
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• 2022

As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.352-357
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• 2023

Since robotic assembly in an unstructured environment is very difficult with existing control methods, studies using artificial intelligence such as reinforcement learning have been conducted. However, since long-time operation of a robot for learning in the real environment adversely affects the robot, so a method to shorten the learning time is needed. To this end, a method based on a pre-trained neural network was proposed in this study. This method showed a learning speed about 3 times than the existing methods, and the stability of reward during learning was also increased. Furthermore, it can generate a more optimal policy than not using a pre-trained neural network. Using the proposed reinforcement learning-based assembly trajectory generator, 100 attempts were made to assemble the power connector within a random error of 4.53 mm in width and 3.13 mm in length, resulting in 100 successes.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.