• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.5
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• pp.394-403
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• 1991

This paper described the development of input driven multilayer perceptron(IDMLP) neural network and it's application to the Korean spoken digit recognition. The IDMPLP neural network used here and the learning algorithm for this network was proposed newly. In this model, weight value is integer and transfer function in the neuron is hard limit function. According to the result of the network learning for the some kinds of input data, the number of network layers is one or more by the difficulties of classifying the inputs. We tested the recognition of binaried data for the spoken digit 0 to 9 by means of the proposed network. The experimental results are 100% and 96% for the learning data and test data, respectively.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.940-945
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• 2021

Future network architectures such as Named Data Networking (NDN) were born to change the way data can be transmitted from current host-centric network technologies to information-centric network technologies. Recently, many studies are being conducted to graft Vehicular NDN to the communication network technology of smart vehicles including connected vehicles. Explosion of data traffic due to Interest/Data packet broadcasting in Vehicular NDN environment is a very important problem to be solved in order to realize VNDN-based data communication. In this paper, the generation of data packet copies according to the increase in network size, vehicle speed, and frequency of interest packets in VNDN network is simulated and evaluated using ndnSIM, in order to show how severe the data broadcast storm phenomenon. The CDP(Copies of Data Packets) increased proportionally in the increase of network size or Interest frequency.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.142-144
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• 2004

To provide more qualified data service on the HFC(Hybrid-Fiber Coaxial) access network, the channel characteristics of upstream transmission band should be carefully investigated and analysed. It will be easier to do network management if the monitoring system for noise measurement in the network is available, In this paper, noise analysis method and the frequency selection method in the upstream band for duplex transmission are suggested. And, Data aquisition device for the signal measurement Is implemented. With this network monitoring system, field test and the result from the collected data are described.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.230-235
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• 1992

We propose a method of generating data to train a neural network controller. The data can be prepared directly by an iterative learning technique which repeatedly adjusts the control input to improve the tracking quality of the desired trajectory. Instead of storing control input data in memory as in iterative learning control, the neural network stores the mapping between the control input and the desired output. We apply this concept to the trajectory control of a two link robot manipulator with a feedforward neural network controller and a feedback linear controller. Simulation results show good generalization of the neural network controller.

• Journal of Information Processing Systems
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• v.4 no.4
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• pp.133-144
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• 2008

Clusters have become a popular alternative for building high-performance parallel computing systems. Today's high-performance system area network (SAN) protocols such as VIA and IBA significantly reduce user-to-user communication latency by implementing protocol stacks outside of operating system kernel. However, emerging parallel applications require a significant improvement in communication latency. Since the time required for transferring data between host memory and network interface (NI) make up a large portion of overall communication latency, the reduction of data transfer time is crucial for achieving low-latency communication. In this paper, Eager Data Transfer (EDT) mechanism is proposed to reduce the time for data transfers between the host and network interface. The EDT employs cache coherence interface hardware to directly transfer data between the host and NI. An EDT-based network interface was modeled and simulated on the Linux-based, complete system simulation environment, Linux/SimOS. Our simulation results show that the EDT approach significantly reduces the data transfer time compared to DMA-based approaches. The EDTbased NI attains 17% to 38% reduction in user-to-user message time compared to the cache-coherent DMA-based NIs for a range of message sizes (64 bytes${\sim}$4 Kbytes) in a SAN environment.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.331-337
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• 2004

In the analysis of massive volume data, a neural network model is a useful tool. To implement the Neural network model, it is important to select initial value. Since the initial values are generally used as random value in the neural network, the convergent performance and the prediction rate of model are not stable. To overcome the drawback a possible method use samples randomly selected from the whole data set. That is, coefficients estimated by logistic regression based on the samples are the initial values.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.177-178
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• 2005

Wireless 1394 over IEEE802.15.3 must allow a data reserved for delivery over a wired 1394 network to be delivered over an IEEE802.15.3 wireless network through bridging IEEE 1394 to IEEE802.15.3. Isochronous transfers on the 1394 bus guarantee timely delivery of data. Specifically, isochronous transfers are scheduled by the bus so that they occur once every $125\;{\mu}s$ and require clock time synchronization to complete the real-time data transfer. IEEE1394.1 and Protocol Adaptation Layer for IEEE1394 over IEEE802.15.3 specify clock time synchronization for a wired 1394 bus network to a wired 1394 bus network and wireless 1394 nodes, which are IEEE802.15.3 nodes handling 1394 applications, over IEEE802.15.3. Thus, the clock time synchronizations are just defined within a homogeneous network environment like IEEE1394 or IEEE802.15.3 until now. This paper proposes new clock time synchronization method for wireless 1394 heterogeneous networks between 1394 and 802.15.3. If new method is adopted for various wireless 1394 products, consumer electronics devices such as DTV and Set-top Box or PC devices on a 1394 bus network can transmit real time data to the AV devices on the other 1394 bus in a different place via IEEE 802.15.3.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.67-75
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• 2010

Network reprogramming is a technology that allows several sensor nodes deployed in sensor field to be repaired remotely. Unlike general communication in sensor network where small amount of data is transferred, network reprogramming requires reliable transfer of large amount of data. The existing network reprogramming techniques suffers high cost and large energy consumption to recover data loss in node communication. In this paper, a cluster based network reporgramming scheme is proposed for sensor network. It divides sensor field into several clusters and chooses a cluster header in charge of data relay to minimize duplicated transmission and unnecessary competition. It increases reliability by effective error recovery through status table.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.58-69
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• 2006

This paper presents neural network-based recognition system for automatic light emitting diode (LED) inspection. The back-propagation neural network (BPNN) is proposed and tested. The current-voltage (I-V) characteristic data of LED from the inspection process is used for the network training and testing. This study selects 300 random samples as network training and employs 100 samples as network testing. The experimental results show that if the classification work is done well, the accuracy of recognition is 100%, and the testing speed of the proposed recognition system is almost one half faster than the traditional inspection system does. The proposed neural-network approach is successfully demonstrated by real data sets and can be effectively developed as a recognition system for a practical application purpose.