• Computers and Concrete
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• v.21 no.5
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• pp.525-530
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• 2018

As a nondestructive testing method, the Schmidt rebound hammer is widely used for structural health monitoring. During application, a Schmidt hammer hits the surface of a concrete mass. According to the principle of rebound, concrete strength depends on the hardness of the concrete energy surface. Study aims to identify the main variables affecting the results of Schmidt rebound hammer reading and consequently the results of structural health monitoring of concrete structures using adaptive neuro-fuzzy inference system (ANFIS). The ANFIS process for variable selection was applied for this purpose. This procedure comprises some methods that determine a subsection of the entire set of detailed factors, which present analytical capability. ANFIS was applied to complete a flexible search. Afterward, this method was applied to conclude how the five main factors (namely, age, silica fume, fine aggregate, coarse aggregate, and water) used in designing concrete mixture influence the Schmidt rebound hammer reading and consequently the structural health monitoring accuracy. Results show that water is considered the most significant parameter of the Schmidt rebound hammer reading. The details of this study are discussed thoroughly.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.467-467
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• 2007

Solid insulation exposed to voltage is degraded by electrical tree process. And the degradation of the insulation is accelerated by voltage application. For this experimental, specimen of electrical tree model is made by XLPE (cross-linked polyethylene). And the size of the specimen is $7^*5^*7mm^3$. Distance between needle and plane is 2 mm. Voltages applied to acceleration test are ranged 12 to 15 kV. And distribution characteristic of degraded stage is studied too. By PD detecting and data processing, discharge data was acquired from PD detecting system (Biddle instrument). The system presents statistical distribution of phase resolved. Moreover, the processing time of electrical tree is recorded to know the speed of degradation according to voltage. Finally, it's used PD classification by ANFIS method.

• Journal of Korea Water Resources Association
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• v.51 no.11
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• pp.1031-1041
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• 2018

ANFIS-based methodology for improving GloSea5 ensemble weather forecast is developed and evaluated in this study. The proposed method consists of two steps: pre & post processing. For ensemble prediction of GloSea5, weights are assigned to the ensemble members based on Optimal Weighting Method (OWM) in the pre-processing. Then, the bias of the results of pre-processed is corrected based on Model Output Statistics (MOS) method in the post-processing. The watershed of the Chungju multi-purpose dam in South Korea is selected as a study area. The results of evaluation indicated that the pre-processing step (CASE1), the post-processing step (CASE2), pre & post processing step (CASE3) results were significantly improved than the original GloSea5 bias correction (BC_GS5). Correction performance is better the order of CASE3, CASE1, CASE2. Also, the accuracy of pre-processing was improved during the season with high variability of precipitation. The post-processing step reduced the error that could not be smoothed by pre-processing step. It could be concluded that this methodology improved the ability of GloSea5 ensemble weather forecast by using ANFIS, especially, for the summer season with high variability of precipitation when applied both pre- and post-processing steps.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.259-262
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian menbership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.1
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• pp.30-37
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• 2000

We studied on acquiring of transfer function and tuning of 2-DOF PID controller using ANFIS for the optimum control to turbine's variables variety. Since the shape of a membership function in the ANFIS based on the characteristics of plant. ANFIS based control method is effective for plant that its variable vary. On the other hand, a start-up time is very short and its variable's value for optimal start-up in gas turbine should be varied, but it is very difficult for such a controller to design. In this paper, we tune 2-DOF PID controller after apply a ANFIS to the operating data of Gun-san gas turbine and verify the characteristics. Its results is compared to the conventional PID controller and discuss. We expect this method will be used for another process because it is studied on the real operating data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.123-123
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• 2018

가뭄은 홍수와 달리 진행속도가 비교적 느리기 때문에 초기에 감지한다면 피해를 최소화 할 수 있다. 국내에서는 가뭄전망을 위해 물리적 기반의 기상-수문연계해석 시스템을 구축하여 월 내지 계절전망을 수행하고 있다. 물리적 기반의 가뭄전망은 수치예보모델의 불확실성을 가지고 있으므로 예보 정확도 개선의 측면에서는 통계적 모델을 같이 활용하는 것이 바람직하다. 최근 국외에서는 통계적 방법인 AI (Artificial Intelligence) 기술을 사용하여 가뭄을 전망하는 연구가 활발히 진행 중이나, 아직까지 국내에서는 관련연구가 미흡한 실정이다. 이에 본 연구에서는 ANFIS (Adaptive Neuro-Fuzzy Inference System) 기반의 댐 유입량 예측 모델을 구축하고 SRI (Standardized Runoff Index)를 활용하여 수문학적 가뭄전망을 수행하였다. 대상유역은 국내 주요 다목적댐이 위치한 충주댐 유역과 소양강댐 유역을 선정하였다. 수문 및 기상자료는 국토 교통부 및 기상청의 관측 댐 유입량, 관측 강수량, 관측 기온 및 장기기상예보 자료를 사용하였다. ANFIS 모델 구축을 위한 훈련 및 보정기간과 검정기간은 각각 1987~2010년과 2011~2016년을 선정하였다. 수문학적 가뭄전망은 지속기간 3개월의 1개월 전망 SRI3를 활용하였으며, SRI3는 관측유입량과 예측유입량을 결합하여 산정하였다. 댐 예측유입량 및 수문학적 가뭄전망의 정확도 평가를 위해 상관계수, 평균제곱근오차를 활용하였다. 댐 예측유입량 평가 결과 예측값과 관측값의 상관계수가 높게 나타났으며, 평균제곱근오차는 낮아 예측성이 뛰어났다. SRI3의 경우 관측값과 예측값의 가뭄발생시기가 유사하여 가뭄을 적절하게 반영하는 것으로 나타났다. 본 연구의 결과는 통계적 기반의 수문학적 가뭄전망기법을 개발하였다는 측면에서 의의가 있으며, 향후 물리적 기반의 가뭄전망정보와 결합한다면 보다 실효성이 향상될 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.51 no.7
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• pp.607-616
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• 2018

Flow regime-based ANFIS Dam Inflow Prediction (FADIP) model is developed and compared with ANFIS Dam Inflow Prediction (ADIP) model in this study. The selected study area is the Chungju and Soyang multi-purpose dam watersheds in South Korea. The dam inflow, precipitation and monthly weather forecast information are used as input variables of the models. The training and validation periods of the models are 1987~2010 for Chungju and 1984~2010 for Soyang dam watershed. The testing periods for both watersheds are 2011~2016. The results of training and validation indicate that FADIP has better training ability than ADIP for predicting dam inflow in normal and low flow regimes. In the result of testing, ADIP shows low predictability of dam inflow in the low flow regime due to the model tuning on all flow regime together. However, FADIP demonstrates the improved accuracy over the entire period compared to ADIP, especially during the normal and low flow seasons. It is concluded that FADIP is valuable for the prediction of dam inflow in the case of drought years, and useful for water supply management of the multi-purpose dam.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.104-114
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• 2018

Recently, as HTTP-based video streaming traffic continues to increase, Dynamic Adaptive Streaming over HTTP(DASH), which is one of the HTTP-based adaptive streaming(HAS) technologies, is receiving attention. Accordingly, many video quality control techniques have been proposed to provide a high quality of experience(QoE) to clients in a DASH environment. In this paper, we propose a new quality control method using ANFIS(Adaptive Network based Fuzzy Inference System) which is one of the neuro-fuzzy system structure. By using ANFIS, the proposed scheme can find fuzzy parameters that selects the appropriate segment bitrate for clients. Also, considering the characteristic of VBR video, the next segment download time can be more accurately predicted using the actual size of the segment. And, by using this, it adjusts video quality appropriately in the time-varying network. In the simulation using NS-3, we show that the proposed scheme shows higher average segment bitrate and lower number of bitrate-switching than the existing methods and provides improved QoE to the clients.

• Journal of Korea Water Resources Association
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• v.37 no.6
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• pp.449-460
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• 2004

The purpose of this study is to test the applicability of neuro-fuzzy system for monthly dam inflow forecasts by using weather forecasting information. The neuro-fuzzy algorithm adopted in this study is the ANFIS(Adaptive neuro-fuzzy Inference System) in which neural network theory is combined with fuzzy theory. The ANFIS model can experience the difficulties in selection of a control rule by a space partition because the number of control value increases rapidly as the number of fuzzy variable increases. In an effort to overcome this drawback, this study used the subtractive clustering which is one of fuzzy clustering methods. Also, this study proposed a method for converting qualitative weather forecasting information to quantitative one. ANFIS for monthly dam inflow forecasts was tested in cases of with or without weather forecasting information. It can be seen that the model performances obtained from the use of past observed data and future weather forecasting information are much better than those from past observed data only.