• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.1-10
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• 2018

Recently, localized heavy rainfall has led to increasing flood damage in urban areas such as Gangnam, Seoul ('12), Busan ('13), Ulsan ('16) Incheon and Busan ('17) etc. Urban flooding occurs relatively rapidly compared to flood damage in river basin, and property damage including damage to houses, cars and shopping centers is more serious than facility damage to structures such as levees and small bridges. In Korea, heavy rain warnings are currently announced using the criteria set by KMA (Korea Meteorological Administration). However, these criteria do not reflect regional characteristics and are not suitable to urban flood. So in this study, estimated the flooding limit rainfall amount based on the damage records for Seoul and Ulsan. And for regions that can not estimate the flooding limit rainfall since there is no damage records, we estimated the flooding limit rainfall using a Neuro-Fuzzy model with runoff characteristics. Based on the estimated flooding limit rainfall, the urban flood warning criteria was set. and applied to the actual flood event. As a result of comparing the estimated flooding limit rainfall with the actual flooding limit rainfall, the error of 1.8~20.4% occurred. And evacuation time was analyzed from a minimum of 28 minutes to a maximum of 70 minutes. Therefore, it can be used as a warning criteria in the urban flood.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.861-876
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• 2008

This paper concerns a new method called Fuzzy Supervised Method for error matrix, the method has developed based on Adoptive Neuro- Fuzzy Inference Systems(ANFIS). For the performance point of view initially the new method tested with trial data and then this paper applies the proposed method with real world problems. So that this paper generated 1000 random error matrices in programming language [R] and then it tests the new proposed method for the error matrices. The results of Fuzzy Supervised Method given in terms of Kappa Index and Congalton Accuracy Indexes, and performance of Fuzzy Supervised Method has evaluated by using Pearson's test.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.161-164
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• 2004

The yo-yo system has been introduced as an interesting plant to demonstrate the effectiveness of intelligent controllers. Having nonlinear and asymmetric characteristics, the yo-yo plant requires a controller quite different from conventional controllers such as PID. In this paper is presented an adaptive method of controlling the yo-yo system. Fuzzy logic controller based on human expertise is referred at first. Then, an adaptive fuzzy controller which has adaptation features against the variation of plant parameters is proposed. Finally, experimental results are presented.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.170-173
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• 2002

본 논문에서는 여러 분야에서 널리 응용되고 있는 적응 뉴로-퍼지 시스템(ANFIS)에서의 효과적인 퍼지 규칙 생성방법을 제안한다. ANFIS의 성능 개선을 위해 구조동정을 수행함에 있어서 전제부 파라미터는 EM(Expectation-Maximization) 알고리즘을 적용하였으며, 파라미터학습은 Jang에 의한 하이브리드 방법을 적용한다. 여기서 초기의 중심과 분산을 구하기 위해 FCM(Fuzzy c-means) 클러스터링 기법을 사용하였다. 이렇게 함으로서 적은 규칙 수를 가지면서도 효율적인 퍼지 규칙을 얻을 수 있도록 하였다. 이들 방법의 유용함을 보이고자 Box-Jenkins의 가스로 데이터에 적용하여 제안된 방법이 이전의 연구보다 좋은 결과를 보임을 보이고자 한다