• 전자공학회논문지B
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• 제32B권3호
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• pp.130-138
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• 1995

In this paper, a Novel Approach is Proposed which Identifies linear system Parameters Using a multilayer feedforward neural network trained with backpropagation algorithm. The parameters of linear system can be represented by x9t)/x(t) and x(t)/u(t). Thud, its parameters can be represented in terms of the derivative of output with respect to input of parameters can be represented in terms of the derivative of output with respect to input of trained neural network which is a function of weights and output of neurons. Mathematical representation of the proposed approach is derived, and its validity is shown by simulation results on 2-layer and 3-layer neural network.

• 환경영향평가
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• 제18권5호
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• pp.271-280
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• 2009

The objective of this study is to develop a reliable flow forecasting model based on neural network algorithm in order to provide flow rate at stream sections without flow measurement in Nakdong river. Stream flow rate measured at 8-days interval by Nakdong river environment research center, daily upper dam discharge and precipitation data connecting upstream stage gauge were used in this development. Back propagation neural network and multi-layer with hidden layer that exists between input and output layer are used in model learning and constructing, respectively. Model calibration and verification is conducted based on observed data from 3 station in Nakdong river.

• 한국경영과학회지
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• 제24권4호
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• pp.111-122
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• 1999

In this paper, we consider a queueing network model. where the population constraint within each subnetwork is controlled by a semaphore queue. The total number of customers that may be present in the subnetwork can not exceed a given value. Each node has a constant service time and the arrival process to the queueing network is an arbitrary distribution. A major characteristics of this model is that the lower layer flow is halted by the state of higher layer. We present some properties that the inter-change of nodes does not make any difference to customer's waiting time in the queueing network under a certain condition. The queueing network can be transformed into a simplified queueing network. A dramatic simplification of the queueing network is shown. It is interesting to see how the simplification developed for sliding window flow control, can be applied to multi-layered queueing network.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권9호
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• pp.423-430
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• 2002

The fabrication and experimentation of multi-channel electrodes which enable detecting and recording of multi-site neuronal signals have been investigated. A multi-channel electrode array was fabricated by depositing 2000${\AA}$ thick Au layer on the 1000${\AA}$ thick Ti adhesion layer on a glass wafer. The metal paths were patterned by wet etching and passivated by depositing a PECVD silicon nitride insulation layer to prevent signals from intermixing or cross-talking. After placing a thin slice of rat cerebellar granule cell in the culture ring located in central portion of the multi-channel electrode plate, a neuronal signal from an electrode which is in contact with the cerebellar granule cell has been detected. It was found that the electrode impedance ranges 200㏀∼1㏁ and the impedance is not changed by cleaning with nitric acid. Also, the impedance is inversely proportion to the exposed electrode area and the cross-talk is negligible when the electrode spacing is bigger than 600$\mu\textrm{m}$. The amplitude and frequency of the measured action potential were 38㎷ and 2㎑, which are typical values. From the experimental results, the fabricated multi-channel electrode array proved to be suitable for multi-site neuronal signal detection for the analysis of a complicated cell network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1620-1634
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• 2023

This paper studies the task of embedding nodes with multiple graphs representing multiple information channels, which is useful in a large volume of network clustering tasks. By learning a node using multiple graphs, various characteristics of the node can be represented and embedded stably. Existing studies using multi-channel networks have been conducted by integrating heterogeneous graphs or limiting common nodes appearing in multiple graphs to have similar embeddings. Although these methods effectively represent nodes, it also has limitations by assuming that all networks provide the same amount of information. This paper proposes a method to overcome these limitations; The proposed method gives different weights according to the source graph when embedding nodes; the characteristics of the graph with more important information can be reflected more in the node. To this end, a novel method incorporating a multi-channel gate layer is proposed to weigh more important channels and ignore unnecessary data to embed a node with multiple graphs. Empirical experiments demonstrate the effectiveness of the proposed multi-channel-based embedding methods.

• Journal of information and communication convergence engineering
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• 제18권2호
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• pp.82-87
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• 2020

This paper discusses how to design a game server that is scalable, adaptable, and re-buildable with components. Furthermore, it explains how various implementation issues were resolved. To support adaptability, the server comprises three layers: network, user, and database. To ensure independence between the layers, each layer was designed to communicate with each other only via message queues. In this architecture, each layer can have an arbitrary number of threads; thus, scalability is guaranteed for each layer. The network layer uses input/output completion ports(IOCP), which shows the best performance on the Windows platform, it can handle up to 5,000 simultaneous connections on a typical entry-level computer, despite being built with a single-threaded user layer. To completely separate the database from the game server, the SQL code was not directly embedded in the database layer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5049-5062
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• 2016

This paper analyzes the secrecy performance of an amplify-and-forward (AF) relay network, where a multi-antenna eavesdropper attempts to overhear the transmitted message from a multi-antenna source to a multi-antenna destination with a single antenna relay. Firstly, we derive the approximate analytical expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR) of the relay network. Then, asymptotic expressions of SOP and ASR at high main-to-eavesdropper ratio (MER) are also provided to reveal the diversity gain of the secure communication. Finally, numerical results are given to verify the theoretical analysis and show the effect of the number of antennas in the considered relay network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3729-3749
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• 2021

At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods.