• Title/Summary/Keyword: neural computing

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Design of Multi-Dynamic Neural Network Controller using Nonlinear Control Systems (비선형 제어 시스템을 이용한 다단동적 신경망 제어기 설계)

  • Rho, Yong-Gi;Kim, Won-Jung;Cho, Hynu-Seob
    • Proceedings of the KAIS Fall Conference
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    • 2006.11a
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    • pp.122-128
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    • 2006
  • The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

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Sub-Frame Analysis-based Object Detection for Real-Time Video Surveillance

  • Jang, Bum-Suk;Lee, Sang-Hyun
    • International Journal of Internet, Broadcasting and Communication
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    • v.11 no.4
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    • pp.76-85
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    • 2019
  • We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

Context-Aware Framework Using Context Ontology for Supporting QoS-based Services (상황 온톨로지를 활용한 QoS 제공 상황인식 프레임워크)

  • Seo, Dong-Woo;Lee, Jae-Yeol
    • IE interfaces
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    • v.21 no.1
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    • pp.65-74
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    • 2008
  • In the future, the ubiquitous computing environment will provide users with context-aware services, intelligently interacting with omnipresent resources and surrounding environments at any location and time. Therefore, the ubiquitous computing environment requires context-aware applications in order to gather and analyze context information in various situations. However, existing context-aware applications have mainly focused on providing services and adapting themselves to users based on user related contexts. But this environment requires not only user related contexts but also Quality-of-Service (QoS) related contexts. In this paper we propose a frame-work for supporting user-oriented QoS in ubiquitous environments. The proposed approach adopts a semantic ontology and a neural network algorithm in order to reason about explicit and uncertain contexts. We also show the possibility of applying the framework to real environments such as collaborative engineering service, automobile maintenance service and ubiquitous home service.

COLORNET: Importance of Color Spaces in Content based Image Retrieval

  • Judy Gateri;Richard Rimiru;Micheal Kimwele
    • International Journal of Computer Science & Network Security
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    • v.23 no.5
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    • pp.33-40
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    • 2023
  • The mainstay of current image recovery frameworks is Content-Based Image Retrieval (CBIR). The most distinctive retrieval method involves the submission of an image query, after which the system extracts visual characteristics such as shape, color, and texture from the images. Most of the techniques use RGB color space to extract and classify images as it is the default color space of the images when those techniques fail to change the color space of the images. To determine the most effective color space for retrieving images, this research discusses the transformation of RGB to different color spaces, feature extraction, and usage of Convolutional Neural Networks for retrieval.

Cross-Validation Probabilistic Neural Network Based Face Identification

  • Lotfi, Abdelhadi;Benyettou, Abdelkader
    • Journal of Information Processing Systems
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    • v.14 no.5
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    • pp.1075-1086
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    • 2018
  • In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

A Brief Introduction to Soft Computing

  • Hong Dug Hun;Hwang Changha
    • Proceedings of the Korean Statistical Society Conference
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    • 2004.11a
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    • pp.65-66
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    • 2004
  • The aim of this article is to illustrate what soft computing is and how important it is.

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Wine Quality Classification with Multilayer Perceptron

  • Agrawal, Garima;Kang, Dae-Ki
    • International Journal of Internet, Broadcasting and Communication
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    • v.10 no.2
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    • pp.25-30
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    • 2018
  • This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

A Structure of Spiking Neural Networks(SNN) Compiler and a performance analysis of mapping algorithm (Spiking Neural Networks(SNN)를 위한 컴파일러 구조와 매핑 알고리즘 성능 분석)

  • Kim, Yongjoo;Kim, Taeho
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.5
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    • pp.613-618
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    • 2022
  • Research on artificial intelligence based on SNN (Spiking Neural Networks) is drawing attention as a next-generation artificial intelligence that can overcome the limitations of artificial intelligence based on DNN (Deep Neural Networks) that is currently popular. In this paper, we describe the structure of the SNN compiler, a system SW that generate code from SNN description for neuromorphic computing systems. We also introduce the algorithms used for compiler implementation and present experimental results on how the execution time varies in neuromorphic computing systems depending on the the mapping algorithm. The mapping algorithm proposed in the text showed a performance improvement of up to 3.96 times over a random mapping. The results of this study will allow SNNs to be applied in various neuromorphic hardware.

Efficient Thread Allocation Method of Convolutional Neural Network based on GPGPU (GPGPU 기반 Convolutional Neural Network의 효율적인 스레드 할당 기법)

  • Kim, Mincheol;Lee, Kwangyeob
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.7 no.10
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    • pp.935-943
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    • 2017
  • CNN (Convolution neural network), which is used for image classification and speech recognition among neural networks learning based on positive data, has been continuously developed to have a high performance structure to date. There are many difficulties to utilize in an embedded system with limited resources. Therefore, we use GPU (General-Purpose Computing on Graphics Processing Units), which is used for general-purpose operation of GPU to solve the problem because we use pre-learned weights but there are still limitations. Since CNN performs simple and iterative operations, the computation speed varies greatly depending on the thread allocation and utilization method in the Single Instruction Multiple Thread (SIMT) based GPGPU. To solve this problem, there is a thread that needs to be relaxed when performing Convolution and Pooling operations with threads. The remaining threads have increased the operation speed by using the method used in the following feature maps and kernel calculations.

Experimental Studies of neural Network Control Technique for Nonlinear Systems (신경회로망을 이용한 비선형 시스템 제어의 실험적 연구)

  • Jeong, Seul;Yim, Sun-Bin
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.11
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    • pp.918-926
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    • 2001
  • In this paper, intelligent control method using neural network as a nonlinear controller is presented. Simulation studies for three link rotary robot are performed. Neural network controller is implemented on DSP board in PC to make real time computing possible. On-line training algorithms for neural network control are proposed. As a test-bed, a large x-y table was build and interface with PC has been implemented. Experiments such as inverted pendulum control and large x-y table position control are performed. The results for different PD controller gains with neural network show excellent position tracking for circular trajectory compared with those for PD controller only. Neural control scheme also works better for controlling inverted pendulum on x-y table.

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