• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2070-2079
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• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.716-721
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• 2023

Inverse Text Normalization(ITN) 모델은 음성 인식(STT) 엔진의 중요한 후처리 영역 중 하나이며, STT 인식 결과의 가독성을 개선한다. 최근 ITN 모델에 심층신경망을 활용한 연구가 진행되고 있다. 심층 신경망을 사용하는 대부분의 선행연구는 문장 내 변환이 필요한 부분에 토큰 태깅을 진행하는 방식이다. 그러나 이는 Out-of-vocabulary(OOV) 이슈가 있으며, 학습 데이터 구축 시 토큰 단위의 섬세한 태깅 작업이 필요하다는 한계점이 존재한다. 더불어 선행 연구에서는 STT 인식 결과를 그대로 사용하는데, 이는 띄어쓰기가 중요한 한국어 ITN 처리에 변환 성능을 보장할 수 없다. 본 연구에서는 BART 기반 생성 모델로 생성형 ITN 모델을 구축하였고, Bi-LSTM 기반 보조 신경망 모델을 결합하여 STT 인식 결과에 대한 고유명사 처리, 띄어쓰기 교정 기능을 보완한 모델을 제안한다. 또한 보조 신경망을 통해 생성 모델 처리 여부를 판단하여 평균 추론 속도를 개선하였다. 실험을 통해 두 모델의 각 정량 성능 지표에서 우수한 성능을 확인하였고 결과적으로 본 연구에서 제안하는 두 모델의 결합된 방법론의 효과성을 제시하였다.

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Science of Emotion and Sensibility
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• v.7 no.3
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• pp.15-25
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• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.49-57
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• 1999

The fusion of fuzzy set theory and neural networks technologies have concentrated on applying neural networks to obtain the optimal rule bases of fuzzy logic system. Unfortunately, this is very hard to achieve due to limited learning capabilities of neural networks. To overcome this difficulty, we propose a new approach in which rough set theory and neuro-fuzzy fusion are combined to obtain the optimal rule base from input/output data. Compared with conventional FNN, the proposed algorithm is considerably more realistic because it reduces overlapped data when construction a rule base. This results are applied to the construction of inference rules for controlling the temperature at specified points in a refrigerator.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.1022-1031
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• 2009

In this paper, we propose a researcher modeling technique based on ontology and construct social network for researchers using diverse relational properties. User ontology schema is created by extending the existing HR-XML model for a researcher model. User ontology schema and instance are created by OWL. We compose social network model for efficient cooperation between researchers using static relational properties such as educational background and dynamic relational properties such as co-authors and co-workers, etc. Closeness has direction because researcher network is differently configured by the researchers. We define inferencing rules using SWRL and inference ontology rules using racer inference machine to compose direct relationships between researchers. The proposed model for researchers can be applied to the cooperation model for researchers by retrieving common expert group dynamically.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.229-233
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• 2006

기계번역(Machine Translaton, MT), 다국어 정보 검색, 의미 정보 검색 등에 대한 연구는 시소러스, 지식베이스, 사전 검색, 의미망, 코퍼스등과 같은 다양한 방법으로 이루어지고 있다. 시맨틱 웹이 등장과 시맨틱 웹 기반 기술의 발전에 따라 위 연구들을 시맨틱 웹에 적용시킬 필요성도 제안되었다. 특히 한국어 시소러스, 워드넷(WordNet), 전자 세종 사전, 가도까와(Kadokawa) 시소러스와 같은 지식베이스가 개발되었으나 활용 분야에 따라 그 구축 방법론이 다르게 적용되어, 위 연구에 효과적으로 통용될 수 있는 지식베이스는 실질적으로 구축되지 못한 실정이다. 따라서 본 연구에서는 세종 사전과 가도까와 시소러스, 한/일 기계 번역 사전 그리고 전문 용어 사전을 기반으로 한국어와 일본어 지식베이스를 위한 사전 온톨로지 서버를 정의하여 의미 정보를 구성하고, Semantic Web Rule Markup Language (이하 SWRL)을 이용해 구문 정보 규칙을 정의한다. 그리고 SWRL 기반 정방향 추론 엔진을 이용하여 번역에 필요한 추론 엔진을 구성하고 문장 구문형성 규칙 추론 엔진을 통해 사용자에게 한국어와 일본어의 문장 구성 변환을 제공한다. 본 연구는 현재 기계 번역이 갖고 있는 다의성, 술부 어순의 차이, 경어체 등 아직 해결해야 할 많은 부분들에 대한 해결 방안으로서 시맨틱 웹 기반 기술과의 활용방안을 제시하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.342-347
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• 2003

It is essential to develop an algorithm for the estimate of link travel velocity and for the supply and control of travel information in the context of intelligent transportation information system. The paper proposes the fuzzy logic based prediction of link travel velocity. Three factors such as time, date and velocity are considered as major components to represent the travel situation. In the fuzzy modeling, those factors were expressed by fuzzy membership functions. We acquire position／velocity data through GPS antenna with PDA embedded probe vehicles. The link travel velocity is calculated using refined GPS data and the prediction results are compared with actual data for its accuracy.