• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.12-17
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• 2002

We propose a new category of neurofuzzy networks- Self-organizing Neural Networks(SONN) with fuzzy polynomial neurons(FPNs) and discuss a comprehensive design methodology supporting their development. Two kinds of SONN architectures, namely a basic SONN and a modified SONN architecture are dicussed. Each of them comes with two types such as the generic and the advanced type. SONN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulation involves a series of synthetic as well as experimental data used across various neurofuzzy systems. A comparative analysis is included as well.

• 한국차세대컴퓨팅학회논문지
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• 제15권2호
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• pp.7-16
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• 2019

신경 회로망을 구현하기 위해 다양한 시도들이 이루어지고 있으며, 하드웨어적인 개선을 위해 전용 칩 개발이 이루어지고 있다. 이러한 신경 회로망을 웨어러블 디바이스에 적용하기 위해서는 소형화와 저전력 동작이 필수적이다. 이러한 관점에서 적합한 구현 방법은 FPGA (field programmable gate array)를 사용한 디지털 회로 설계이다. 이 시스템을 구현하기 위해서는 성능 향상을 위해 신경 회로망의 많은 부분을 차지하는 학습 알고리즘을 FPGA 내에 구현하여야 한다. 본 논문에서는 FPGA를 이용하여 다양한 학습 알고리즘 중 역전파 알고리즘을 구현하였으며, 구현 된 신경 회로망은 OR 게이트 연산을 통해 검증되었다. 또한 이러한 신경 회로망을 활용하여 다양한 사용자의 생체 신호 측정 결과를 분석할 수 있음을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.195-195
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• 2000

In this paper, intelligent control method using neural network as a nonlinear controller is presented, Neural network controller is implemented on DSP board in PC to make real time computing possible, On-line training algorithm for neural network control is proposed, As a test-bed, a large a-x table was build and interface with PC has been implemented, Experimental results under different PD controller gains show excellent position tracking for circular trajectory compared with those for PD controller only.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 추계학술발표논문집 1부
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• pp.344-348
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• 2010

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.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2010년도 추계학술발표논문집 1부
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• pp.332-336
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• 2010

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.

• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2008년도 추계학술발표논문집
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), 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 DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2009년도 춘계학술발표논문집
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• pp.454-457
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• 2009

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.