• Current Optics and Photonics
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• v.1 no.5
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• pp.456-467
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• 2017

The benefits of network coding in all-optical multicast networks have been widely demonstrated. In this paper, we mainly discuss the multicast service efficiently provisioning problem in the network coding enabled elastic optical networks (EONs). Although most research on routing and spectrum allocation (RSA) has been widely studied in the elastic optical networks (EONs), rare research studies RSA for multicast in the network coding enabled EON, especially considering the time delay constraint. We propose an efficient heuristic algorithm, called Network Coding based Multicast Capable-Multipath Routing and Spectrum Allocation (NCMC-MRSA) to solve the multipath RSA for multicast services in the network coding enabled EON. The well-known layered graph approach is utilized for NCMC-MRSA, and two request ordering strategies are utilized for multiple multicast requests. From the simulation results, we observe that the proposed algorithm NCMC-MRSA performs more efficient spectrum utilization compared with the benchmark algorithms. NCMC-MRSA utilizing the spectrum request balancing (SRB) ordering strategy shows the most efficient spectrum utilization performance among other algorithms in most test networks. Note that we also observe that the efficiency of NCMC-MRSA shows more obvious than the benchmark algorithm in large networks. We also conduct the performance comparisons of two request ordering strategies for NCMC-MRSA. Besides, we also evaluate the impact of the number of the linkdisjoint parallel w paths on the spectrum utilization performance of the proposed algorithm NCMC-MRSA. It is interesting to find that the change of the parameter w in a certain range has a significant impact on the performance of NCMC-MRSA. As the parameter w increases to a certain value, the performances of NCMC-MRSA cannot be affected by the change of w any more.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.97-102
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• 2023

This study is to provide improvement of tester for IoT system, which has recently become diversified and large-scale and It is about a method to improve the packet processing performance of the tester and securing flexibility in traffic protocol creation and operation. The purpose of this study is to design a OSS DPDK-based high-speed IoT network performance test system, which pre-verifies and measures the performance of data traffic transmission in an increasingly sophisticated high-capacity IoT network system. The basic structure of the high-speed IoT performance tester was designed using a DPDK-based traffic generator, the expected effect was suggested to traffic modeling and packet generation capability when the system was applied through experiments

• 한국정보통신설비학회:학술대회논문집
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• 2006.08a
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• pp.87-90
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• 2006

Nowadays, we can enjoy access to high speed network and advanced services of convergence between broadcasting and communication anywhere and anytime through a ubiquitous computing. So, now digital convergence devices come out constantly. These devices are required faster network environment for high speed data processing than before. In this paper, we describe the design of InfiniBnad network adapter, which is included two FPGA chipsets. When this adapter is installed to Digital Convergence Platform (DCP) network performance will be improved. The adapter includes 12channel serial ports for external communication and internally, uses PCI-Express bus. We have finished the test of high speed serial based network adapter through composing complete InfiniBand network and applied fabric management software. So, we have verified that it can be applied on DCP environment.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.38-44
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• 2013

In this paper, we designed and implemented a mobile video transmission device with 1080i resolution and 30fps frame rate using wireless multi-access technologies. The video acquisition delay time of 16us and jitter of 21ms for this equipment were obtained, respectively. The device is designed to access various communication networks. In order to evaluate the performance of its video transmission capability, a server and monitoring viewer are implemented. In the performance test using LTE network, 211ms delay time was obtained at 3Mbps transmission. Rendering of 1fps frame rate at the 265kbps transmission was confirmed for the transmission test using Inmarsat network.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.93-104
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• 2021

In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.389-392
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• 2017

Previously, PCC and RFCC were proposed for the network coding possibility test of flow-intersecting nodes in a multi-hop wireless network. PCC works correctly only if there is only one intersecting node. RFCC solves this problem by defining the network coding conditions with considering decoding possibility at intermediate nodes. However, this may increase decoding possibility test overhead and coding operations at intermediate nodes. In this paper, we define DCC which can decrease this overhead by allowing decoding only at destinations. We analyze the performance of DCC by simulations.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.1
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• pp.29-38
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• 2010

It is highly requested that the more complicated embedded software is used for high performance and multiple functions of the systems. This is inevitably increasing the errors. Therefore the embedded software testing is getting important recently. There are not general testing methods which are able to be applied for any embedded systems, but via this research, we introduce a testing method which is based on TTCN-3, a testing standard, for embedded systems. A testing environment for network-based embedded software is implemented with considering the features of TTCN-3 testing which is based on message exchange. The testing environment has two additional parts with TTCN-3 test system, the network analyzer to access the network-based systems and the communication interface which is suggested for embedded systems in previous work, and we have implemented the whole testing environment with interacting these two parts. In addition to the normal testing domain, called single node testing as a unit testing of V-model, we suggest another concept to test multiple nodes in network. It could be achieved by adding keywords such as supervisor and object which are describing the feature of TTCN-3 testing component and generating the TTCN-3 Executable code which contains new keywords. The testing has done for embedded software which is based on CAN network and the demonstration of the testing environment has been shown in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.625-633
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• 2004

In this paper, position control of a car-like mobile robot using a neural network is presented. positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as a reference, the robot posture is corrected by a cascaded controller. To improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The neural network functions as a compensator to minimize the positional errors in on-line fashion. A car-like mobile robot is built as a test-bed and experimental studies of several controllers are conducted and compared. Experimental results show that the best position control performance can be achieved by a cascaded controller with a neural network.