• IE interfaces
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• v.10 no.3
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• pp.125-132
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• 1997

We proposed a 2-step neural network approach for estimating machining parameters of electric discharge wire cut. The first step net, which is described as a backward neuro-estimation, is designed for estimating coarse cutting parameters while the second phase net, as a polishing forward neuro-estimation, is utilized for determining fine parameters. Sequential estimation procedure, based on backward and forward net, is performed using the net's approximation capability which is M to 1 and 1 to M mapping property. Experimental results an given to evaluate the accuracy of the proposed 2-step neuro-estimation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.05a
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• pp.321-326
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• 1998

본 논문에서는 정밀 제어와 온-라인 제어를 위하여 유전 알고리즘을 이용한 퍼지-신경망 제어기를 제안하였다. 제안된 제어기의 설계방법은 다음과 같은 3단계의 동조과정으로 구성한다. 1) 퍼지 제어기의 비퍼지화 연산을 신경망을 이용하여 함수근사화 시킨 후, 퍼지-신경망 제어기를 구성한다. 2) 플랜트에 적합한 퍼지 소속함수의 형태를 얻기 위해 유전 알고리즘을 이용하여 근사화된 퍼지 소속함수를 찾는다. 3) 근사화된 초기 퍼지 소속함수를 퍼지-신경망 제어기에 의해 적응학습으로 최적의 퍼지 소속함수를 얻고, 또한 플랜트의 파라미터 변동이나 외부환경의 변화에 대해 적응할 수 있도록 최적의 퍼지 소속함수를 추정한다. 제안된 제어기의 성능을 평가하기 위하여 DC 서보모터의 속도제어에 적용하였다.

• Korean Journal of Construction Engineering and Management
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• v.19 no.4
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• pp.82-92
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• 2018

Considering that earlier stage of construction project has a great influence on the possibility of lowering of environmental load, it is important to build and utilize system that can support effective decision making at the initial stage of the project. In this study, we constructed an environmental load estimation model that can be used at the early stage of the project using basic design factors. The model was constructed by using the artificial neural network to estimate environmental load by applying to planning stage (ANN-1), basic design stage (ANN-2). The result of test, shows that average of absolute measuring efficiency and standard deviation of ANN-1 and ANN-2 were 11.19% / 5.30% and 9.59% / 3.09% each. This result indicates that the model using the input variables extended with the project progress has high reliability and it is considered to be effective in decision support at the initial design stage of the project.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1447-1451
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• 2008

저수지 군 연계 운영을 위한 각 댐에서의 방류량을 결정하기 위해서는 대개 각 댐의 초기 저수량, 유역 상 하류 댐의 총 저수량, 수요량, 기간별 발전 목표 달성 정도, 그리고 예상되는 미래유입량 등이 추정되어야 한다. 본 연구에서는 댐 군 연계운영을 위한 일별 최적화 모형인 CoMOM(Coordinated Multi-reservoir Operating Model, 4.2)의 상위 단계의 더 큰 단위 기간에 활용될 댐 군 연계 운영 기본 가이드라인을 신경망 기법을 활용하여 도출할 수 있을 지를 실험해 보고자 한다. 이 방법은 기본적으로 CoMOM이 제시하는 일별 운영 계획의 결과가 최선의 정책일것이라는 가정에 근거하고 있다. 즉, 주어진 상황에서 일별 CoMOM이 제시하는 결과를 교사 신호로 하여 신경망 학습을 수행하고, 이 결과를 통해 규칙(Rule)을 생성하는 과정으로 요약할 수 있다. 신경망 분석은 CoMOM이 이수기 모형인 점을 고려하여 이수기만을 대상으로 실험하였으며, 단위 분석기간을 10일로 택하여 미래 10일간의 방류량을 결정하는 것을 목표로 하였다. 신경망 모형의 입력요소로는 각 댐의 초기 유효 저수량, 유역 상 하류 댐의 총 저수량, 10일간의 수요량, 그리고 향후 한달 동안의 예상 유입량을 적용하였고, 출력요소로는 CoMOM에서 제시한 방류량 결과를 사용하였다. 모형의 유효성을 검증하기 위해 한강수계의 이수기를 대상으로 과거의 유입량 자료가 재현된다고 가정하고, 모의운영을 통하여 적합성을 분석하였다. 이를 위해 매일 단위의 실제 댐 군 연계 운영의 상황을 모의할 수 있는 실시간 시뮬레이션을 적용하였으며, 신경망 모형의 운영 기준에 의해 결정된 향후 10일 동안의 총 방류량이 해당기간 동안 동일한 양으로 나누어 방류된다는 가정 하에 모의 운영하였다. 그리고 도출된 운영 결과는 최종적으로 실적과의 평균저수량, 발전량, 여수로 방류량 비교를 통해 평가하였다.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.414-417
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• 1988

신경회로를 모방한 병렬 아날로그-디지탈 변환기를 설계하고 광학적으로 구현하였다. 이 회로의 동작 원리는 주어진 입력 아날로그 값에 대해 출력 bit 들의 디지털 값을 동시에 추정하는 것으로 $2^{N}$ 단계의 구분을 위해서 N개의 단위 소자가 필요하다. 에너지 최소화 방법에 의해 설계된 신경망 아날로그-디지털 변환기와 비교해 볼 때 회로의 구조가 단순하고 출력이 회로의 초기 상태에 관계없이 주어진 입력에 의해 결정된다.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.71-76
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• 2023

In this paper, we propose a smoothed received signal strength indicator (RSSI)-based distance estimation using deep neural network (DNN) for accurate distance estimation in an environment where a single receiver is used. The proposed scheme performs a data preprocessing consisting of data splitting, missing value imputation, and smoothing steps to improve distance estimation accuracy, thereby deriving the smoothed RSSI values. The derived smoothed RSSI values are used as input data of the Multi-Input Single-Output (MISO) DNN model, and are finally returned as an estimated distance in the output layer through input layer and hidden layer. To verify the superiority of the proposed scheme, we compared the performance of the proposed scheme with that of the linear regression-based distance estimation scheme. As a result, the proposed scheme showed 29.09% higher distance estimation accuracy than the linear regression-based distance estimation scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.2
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• pp.7-13
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• 1998

This paper presents a methodology for determining machining conditions in Electric Discharge Wire-Cut. Unification of two phase neural network approach with an automatic generation of machining parameters is designed. The first phase neural network, which is 1 to M backward-mapping neural net, produces approximate machining conditions. Using approximate conditions, all possible conditions are newly created by the proposed automatic generation procedure. The second phase neural net, which is a M to 1 forward-mapping neural net, determines the best one among the generated candidates. Simulation results with ANN are given to verify that the presenting methodology could apply for determining machining parameters in Electric Discharge Wire-Cut.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.374-382
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• 2020

The purpose of this study is to improve the learning speed of an ammunition stockpile reliability classification artificial neural network model by proposing a normalization method that reduces the number of input variables based on the characteristic of Ammunition Stockpile Reliability Program (ASRP) data without loss of classification performance. Ammunition's performance requirements are specified in the Korea Defense Specification (KDS) and Ammunition Stockpile reliability Test Procedure (ASTP). Based on the characteristic of the ASRP data, input variables can be normalized to estimate the lot percent nonconforming or failure rate. To maintain the unitary hypercube condition of the input variables, min-max normalization method is also used. Area Under the ROC Curve (AUC) of general min-max normalization and proposed 2-step normalization is over 0.95 and speed-up for marching learning based on ASRP field data is improved 1.74 ~ 1.99 times depending on the numbers of training data and of hidden layer's node.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.695-713
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• 2023

In coastal waters, phytoplankton,suspended particulate matter, and dissolved organic matter intricately and nonlinearly alter the reflectivity of seawater. Neural network technology, which has been rapidly advancing recently, offers the advantage of effectively representing complex nonlinear relationships. In previous studies, a three-stage neural network was constructed to extract the inherent optical properties of each component. However, this study proposes an algorithm that directly employs a deep neural network. The dataset used in this study consists of synthetic data provided by the International Ocean Color Coordination Group, with the input data comprising above-surface remote-sensing reflectance at nine different wavelengths. We derived inherent optical properties using this dataset based on a deep neural network. To evaluate performance, we compared it with a quasi-analytical algorithm and analyzed the impact of log transformation on the performance of the deep neural network algorithm in relation to data distribution. As a result, we found that the deep neural network algorithm accurately estimated the inherent optical properties except for the absorption coefficient of suspended particulate matter (R² greater than or equal to 0.9) and successfully separated the sum of the absorption coefficient of suspended particulate matter and dissolved organic matter into the absorption coefficient of suspended particulate matter and dissolved organic matter, respectively. We also observed that the algorithm, when directly applied without log transformation of the data, showed little difference in performance. To effectively apply the findings of this study to ocean color data processing, further research is needed to perform learning using field data and additional datasets from various marine regions, compare and analyze empirical and semi-analytical methods, and appropriately assess the strengths and weaknesses of each algorithm.