• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.1
• /
• pp.20-27
• /
• 2008

In this paper, a nonlinear classifier of a feed-forward neural network is implemented on an FPGA chip. The feedforward neural network is implemented in hardware for fast parallel processing. After off line training of neural network, weight values are saved and used to perform forward propagation of neural processing. As an example, AND and XOR digital logic classification is conducted in off line, and then weight values are used in neural network. Experiments are conducted successfully and confirmed that the FPGA neural network hardware works well.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2735-2737
• /
• 2001

비젼 검사시스템에서 기판에 존재하는 납 형상의 패턴을 분류함으로써 사전에 불량을 줄일 수 있다. 이러한 경우 대부분의 불량은 부정확한 납의 위치 및 두께로 인해 발생하게 되는데, 이러한 문제를 해결하기 위해 주어진 경계 내에 불분명하게 형성된 납의 형태 및 두께를 정상과 불량으로 분류하기 위해 무게중심점에 기초한 정합과 XOR연산을 이용한 비젼 검사시스템을 제안하였다. 제안한 비젼 검사시스템을 인쇄회로기판상의 납땜형상 패턴에 적용하여 제안한 방법의 성능을 검증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.7
• /
• pp.1387-1393
• /
• 2007

Even if the linearly separable patterns can be classified by the conventional single layer perceptron, the non-linear problems such as XOR can not be classified by it. A fuzzy single layer perceptron can solve the conventional XOR problems by applying fuzzy membership functions. However, in the fuzzy single layer perception, there are a couple disadvantages which are a decision boundary is sometimes vibrating and a convergence may be extremely lowered according to the scopes of the initial values and learning rates. In this paper, for these reasons, we proposed an enhanced fuzzy single layer perceptron algorithm that can prevent from vibration the decision boundary by introducing a bias term and can also reduce the learn time by applying the modified delta rule which include the learning rates and the momentum concept and applying the new linear activation function. Consequently, the simulation results of the XOR and pattern classification problems presented that the proposed method provided the shorter learning time and better convergence than the conventional fuzzy single layer perceptron.

• Fashion & Textile Research Journal
• /
• v.19 no.6
• /
• pp.786-794
• /
• 2017

The purpose of this study is to analyze and classify breast shape of women aged 11~15 using 3D body scan data. In this study, 250 women's body scans were selected from the 6th Size Korea dataset, and 30 items from each of the scan were measured using RapidForm XOR 3 program. The principal component analysis and cluster analysis were conducted using statistical program SPSS 17.0. The five principal components were identified; breast drooping and breast capacity, size from chest to under bust area, breast protrusion, breast height, and under breast angle & outer distance of breast. As the results of cluster analysis, woman's breast types were classified into four types. The breast type 1 was protrusion type (25.1%) which is considered as the breast maturity stage. The breast type 2 had the most drooped breast covering a large area (20.2%). The breast type 3 had the least prominent breast with a highest nipple point, which was considered as the early breast development stage (38.9%). The breast type 4 had the obesity of the chest and breast circumferences with the slightly prominent and the least drooped breast (15.8%). This study can provide fundamental information to develop sizing system and brassiere pattern for junior girls.