• Title/Summary/Keyword: Cellular neural networks

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The Structure of Reversible DTCNN (Discrete-Time Celluar Neural Networks) for Digital Image Copyright Labeling (디지털영상의 저작권보호 라벨링을 위한 Reversible DTCNN(Discrete-Time Cellular Neural Network) 구조)

  • Lee, Gye-Ho;Han, Seung-jo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.3
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    • pp.532-543
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    • 2003
  • In this paper, we proposed structure of a reversible discrete-time cellular neural network (DTCNN) for labeling digital images to protect copylight. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary pseudo-random images sequences. We presented some, output examples of different kinds of reversible DTCNNs to show their complex behaviors. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Since the reversibility of a reversible DTCNN, the same reversible DTCNN can recover the copyright label from a labeled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. Computer simulation results are presented.

A Design of a Cellular Neural Network for the Real Image Processing (실영상처리를 위한 셀룰러 신경망 설계)

  • Kim Seung-Soo;Jeon Heung-Woo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.283-290
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    • 2006
  • The cellular neural networks have the structure that consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.

Design of CNN Chip with annealing Capability (어닐링 기능을 갖는 CNN칩 설계)

  • 류성환;박병일정금섭전흥우
    • Proceedings of the IEEK Conference
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    • 1998.10a
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    • pp.1041-1044
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    • 1998
  • In this paper the cellular neural networks with annealing capability is designed. The annealing capability helps the networks escape from the local-minimum points and quickly search for the global-minimum point. A 6$\times$6 CNN chip is designed using a $0.8\mu\textrm{m}$ CMOS technology, and the chip area is 2.89mm$\times$2.89mm. The simulation results for hole filling image processing show that the general CNN has a local-minimum problem, but the annealed CNN finds the global-minimum solutions very efficiently.

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Machine-Part Grouping with Alternative Process Plan - An algorithm based on the self-organizing neural networks - (대체공정이 있는 기계-부품 그룹의 형성 - 자기조직화 신경망을 이용한 해법 -)

  • Jeon, Yong-Deok
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.39 no.3
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    • pp.83-89
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    • 2016
  • The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

Design of CNN Chip with Annealing Capability (어닐링 기능을 갖는 셀룰러 신경망 칩 설계)

  • 유성환;전흥우
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.36C no.11
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    • pp.46-54
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    • 1999
  • The output values of cellular neural networks would have errors because they can be stabilized at local minimums depending on the initial states of each cell. So, in this paper, we design the $6\times6$cellular neural networks with annealing capability which guarantees that the outputs reaches the global minimum to have correct output values independent of the initial states of each cell. This chip is designed using a $0.8\mu\textrm{m}$ CMOS technology The designed chip contains about 15,000 transistors and the chip size is about $2.89\times2.89\textrm{mm}^2$. The simulation results of edge extraction and hole filling using the designed circuit show that the outputs values would have errors in un-annealed case, but not in annealed case. In the simulation, the annealing time of $3\musec$ is employed.

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Two Optimization Techniques for Channel Assignment in Cellular Radio Network (본 논문에서는 신경회로망과 유전자 알고리즘을 이용하여 셀룰러 무선채널 할당을 위한 두 가지 최적화 기법)

  • Nam, In-Gil;Park, Sang-Ho
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.2
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    • pp.439-448
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    • 1999
  • In this paper, two optimization algorithms based on artificial neural networks and genetic algorithms are proposed for cellular radio channel assignment problems. The channel assignment process is characterized as minimization of the energy function which represents constraints of the channel assignment problems. All three constraints such as the co-channel constraint, the adjacent channel constraint and the co-site channel constraint are considered. In the neural networks approach, certain techniques such as the forced assignment and the changing cell order are developed, and in the genetic algorithms approach, data structure and proper genetic operators are developed to find optimal solutions, As simulation results, the convergence rates of the two approaches are presented and compared.

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The Proposal of the Robust Fuzzy Wavelet Morphology Neural Networks Algorithm for Edge of Color Image (컬러 영상 에지에 강건한 퍼지 웨이브렛 형태학 신경망 알고리즘 제안)

  • Byun, Oh-Sung
    • Journal of the Korea Society of Computer and Information
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    • v.12 no.2 s.46
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    • pp.53-62
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    • 2007
  • In this paper, it can propose that Fuzzy Wavelet Morphology Neural Networks for the edge detection algorithm with being robustly a unclear boundary parts by brightness difference and being less sensitivity on direction to be detected the edges of images. This is applying the Fuzzy Wavelet Morphology Operator which can be simple the image robustly without the loss of data to DTCNN Structure for improving defect which carrys out a lot of operation complexly. Also, this color image can segment Y image with YCbCr space color model which has a lossless feature information of edge boundary sides effectively. This paper can offer the simulation of color images of 50ea for the performance verification of the proposal algorithm.

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Detecton of OPtical Flow Using Cellular Nonlinear Neural Networks (셀룰라 비선형 회로 구조를 이용한 optical flow 검출)

  • Son, Hong-Rak;Kim, Hyong-Suk
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.3053-3055
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    • 2000
  • The Cellular Nonlinear Networks structure for Distance Transform (DT) and the robust optical flow detection algorithm based on the DT are proposed. The proposed algorithm is for detecting the optical flows on the trajectories only of the feature points. The translation lengths and the directions of feature movements are detected on the trajectories of feature points on which Distance Transform Field is developed. The robustness caused from the use of the Distance Transform and the easiness of hardware implementation with local analog circuits are the properties of the proposed structure, To verify the performance of the proposed structure and the algorithm, simulation has been done about zooming image.

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A Manufacturing Cell Formantion Algorithm Using Neural Networks (신경망을 이용한 제조셀 형성 알고리듬)

  • 이준한;김양렬
    • Korean Management Science Review
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    • v.16 no.1
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    • pp.157-171
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    • 1999
  • In a increasingly competitive marketplace, the manufacturing companies have no choice but looking for ways to improve productivity to sustain their competitiveness and survive in the industry. Recently cellular manufacturing has been under discussion as an option to be easily implemented without burdensome capital investment. The objective of cellular manufacturing is to realize many aspects of efficiencies associated with mass production in the less repetitive job-shop production systems. The very first step for cellular manufacturing is to group the sets of parts having similar processing requirements into part families, and the equipment needed to process a particular part family into machine cells. The underlying problem to determine the part and machine assignments to each manufacturing cell is called the cell formation. The purpose of this study is to develop a clustering algorithm based on the neural network approach which overcomes the drawbacks of ART1 algorithm for cell formation problems. In this paper, a generalized learning vector quantization(GLVQ) algorithm was devised in order to transform a 0/1 part-machine assignment matrix into the matrix with diagonal blocks in such a way to increase clustering performance. Furthermore, an assignment problem model and a rearrangement procedure has been embedded to increase efficiency. The performance of the proposed algorithm has been evaluated using data sets adopted by prior studies on cell formation. The proposed algorithm dominates almost all the cell formation reported so far, based on the grouping index($\alpha$ = 0.2). Among 27 cell formation problems investigated, the result by the proposed algorithm was superior in 11, equal 15, and inferior only in 1.

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Machine Cell Formation using A Classification Neural Network

  • Lee, Kyung-Mi;Lee, Keon-Myung
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.1
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    • pp.84-89
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    • 2004
  • The machine cell formation problem is the problem to group machines into machine families and parts into part families so as to minimize bottleneck machines, exceptional parts, and inter-cell part movements in cellular manufacturing systems and flexible manufacturing systems. This paper proposes a new machine cell formation method based on the adaptive Hamming net which is a kind of neural network model. To show the applicability of the proposed method, it presents some experiment results and compares the method with other cell formation methods. From the experiments, we observed that the proposed method could produce good cells for the machine cell formation problem.