• Proceedings of the Korean Information Science Society Conference
• /
• 2002.10d
• /
• pp.625-627
• /
• 2002

본 논문에서는 의료영상 인식 기술 중 인식률이 뛰어나고 신뢰성 있는 대뇌출혈성 병변인식 시스템을 구현하기 위하여 신호처리 분야에서 주로 사용되는 Wavelet 변환과 신경망 기법을 이용하고자 한다. 그러나 신경망 기법에서 주로 사용되는 비선형 최적화기법인 Gradient descent BP는 최적화 문제점을 해결하기에는 수렴속도가 느리기 때문에 적합하지 않다. 따라서 본 논문에서는 기존 Gradient descent BP를 보완한 Levenberg-Marquardt Back-Propagation을 대뇌출혈성 병변인식에 적용하여 구현함으로써 총 50개의 패턴 중 45개의 영상이 인식에 성공하였고 전체 평균 인식률은 각각 90%와 87%의 인식률을 보였다.

• The KIPS Transactions:PartD
• /
• v.14D no.1 s.111
• /
• pp.139-144
• /
• 2007

In seeking to improve the workload efficiency and inference capability of context-aware systems, we propose a new framework for an advanced teaming mechanism that uses improved bath propagation (BP) algorithm. Even though a learning mechanism is one of the most important parts in a context-aware system, the existing algorithms focused on facilitating systems by elaborating the learning mechanism with user's context information are rare. BP is the most adaptable algorithm for learning mechanism of context-aware systems. By using the improved BP algorithm, the framework we proposed drastically improves the inference capability so that the overall performance is far better than other systems. Also, using the special system cache, the framework manages the workload efficiently. Experiments show that there is an obvious improvement in overall performanre of the context-awareness systems using the proposed framework.

• Journal of Korea Multimedia Society
• /
• v.21 no.4
• /
• pp.471-477
• /
• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.17 no.9
• /
• pp.1006-1012
• /
• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.

• KIEE International Transactions on Electrophysics and Applications
• /
• v.5C no.3
• /
• pp.138-142
• /
• 2005

In this paper, we compared recognition rates between NN(neural networks) and clustering method as a scheme of off-line PD(partial discharge) diagnosis which occurs at the stator coil of traction motor. To acquire PD data, three defective models are made. PD data for classification were acquired from PD detector. And then statistical distributions are calculated to classify model discharge sources. These statistical distributions were applied as input data of two classification tools, BP(Back propagation algorithm) and ANFIS(adaptive network based fuzzy inference system) pre-processed FCM(fuzzy c-means) clustering method. So, classification rate of BP were somewhat higher than ANFIS. But other items of ANFIS were better than BP; learning time, parameter number, simplicity of algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.2
• /
• pp.80-89
• /
• 2014

In this paper, we conceive a context-aware recommendations system for water distribution in future smart water grids, with taking the end users' profiles, water types, network conditions into account. A spectral clustering approach is developed to cluster end users into different communities, based on the end users' common interests in water resources. A back-propagation (BP) neural network is designed to obtain the rating list of the end users' preferences on water resources and the water resource with the highest prediction rating is recommended to the end users. Simulation results demonstrate that the proposed scheme achieves the improved accuracy of recommendation within 2.5% errors notably together with a better user experience in contrast to traditional recommendations approaches.

• Current Optics and Photonics
• /
• v.6 no.5
• /
• pp.479-488
• /
• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.12
• /
• pp.116-123
• /
• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• Proceedings of the KIEE Conference
• /
• 2004.05b
• /
• pp.109-112
• /
• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.11
• /
• pp.3010-3023
• /
• 1998

GA has a fast convergence speed in searching the one point around optimal value. But it's convergence time increase in searching the region around optimal value because it has no regional searching mechanism. BP has the tendency to converge the local minimum because it has global searching mechanism. To overcome these problems, a method in which a genetic algorithm and a back propagation are applied in turn is proposed in this paper. By using a genetic algorithm, we compute optimal synaptic strength and offset value. And then, these values are fed to the input of the back propagation. This proposed method is superior to each above method in improving the convergence speed.