• 한국건설관리학회논문집
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• 제7권4호
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• pp.91-99
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• 2006

본 연구는 신축 교육시설 프로젝트의 개념단계에서 공사비를 예측하기 위한 인공신경망모델의 제안을 목적으로 한다. 현행 공공 교육시설의 개념단계 공사비예측에는 기본인자인 연면적에 의한 단일변수 모델이 적용되고 있다. 그러나 개념단계에서 단일변수 공사비예측모델을 적용하여 예측된 공사비는 그 오차범위가 크고, 실시설계 완료 후 물량산출에 의해 산정된 상세공사비와 비교하여 큰 차이를 보일 경우 프로젝트의 수정이 불가피하며, 이는 프로젝트의 비용을 증가시키고 공기를 지연시킨다. 그러므로 본 연구에서는 교육시설 프로젝트의 사업계획 수립 및 예산확보 과정에서 공사비예측에 적용이 가능한다 변수 인공신경망모델을 제안하였다. 개발된 모델을 평가한 결과 평균오차율이 6.82%로써, 평균 93.18%의 정확도를 기록하였다. 제안된 인공신경망모델은 지난 5년간 신축된 교육시설의 공사예정금액을 실적자료로 사용하여 학습되었기 때문에, 차후 교육시설 신축공사의 예산편성에 그 활용이 기대된다.

• Journal of Information Processing Systems
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• 제9권4호
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• 한국항행학회논문지
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• 제24권1호
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• pp.47-52
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• 2020

도래각추정은 표적으로부터 생성, 혹은 반사된 신호를 분석하여 표적의 방향을 추정하는 것으로 다양한 분야에 활용되고 있다. 인공신경망은 생물의 신경망을 모방한 기계학습의 한 분야로 패턴인식에서 좋은 성능을 보인다. 이러한 인공신경망을 도래각 추정에 활용하는 연구가 진행되어왔으나, 다양한 신호대잡음비 환경에 대응하는데에 제한이 있는 상황이다. 본 논문에서는 도래각 추정을 위한 3단계 인공신경망 알고리듬을 제안한다. 제안하는 알고리듬은 잡음제거과정을 통해 단일 신호대잡음비 환경에서 학습한 모델을 다양한 환경에 적용해도 성능감소를 최소화할 수 있다. 또한 도래각 시프트 과정을 통해 학습 난이도를 낮출 수 있고 효율적인 추정이 가능하다. 우리는, 제안하는 알고리듬과 다른 부공간 기법, Cramer-Rao bound (CRB)와의 성능 비교를 통해 제안하는 알고리듬이 낮은 신호대잡음비 환경, 표적들의 도래각이 가까운 환경 등 특정한 열악한 관측환경에서 타 기법에 비해 좋은 성능을 보이는 것을 확인하였다.

• 한국정보처리학회논문지
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• 제4권7호
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• pp.1881-1892
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• 1997

본 논문에서는 확률적 VQ 네트워크와 계층적 구조를 가지는 다단계 인식기를 이용한 인쇄체 한자 인식 방법을 제안한다. 대용량 신경망은 구현하기가 매우 어렵기 때문에 모듈화된 신경망을 이용하였으며, 이 과정에서 발생되는 문제점을 확률적 신경망 모델을 이용으로 제거하였다. 또한 엔트로피 이론을 적용하여 오인식률이 높은 혼동 문자쌍에 대하여 재분류를 수행하였다. 실험대상은 KSC5601 코드의 한자 4,888자 중, 동자이음문자를 제외한 4,619자로 하였으며, 학습 데이타와 실험 데이타에 대하여 실험결과, 각각 평균 99.33%, 92.83%의 인식률과 초당 4-5자의 인식속도를 얻음으로써 본 방법의 유효성을 보였다.

• Design & Manufacturing
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• 제17권4호
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권4호
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• 산업경영시스템학회지
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• 제21권48호
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• pp.123-132
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• 1998

The group formation problem of the machine and part is a very important issue in the planning stage of cellular manufacturing systems. This paper investigates Self-Organizing Map(SOM) neural networks approach to machine-part grouping problem. We present a two-phase algorithm based on SOM for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. Output layer in SOM network is one-dimensional structure and the number of output node has been increased sufficiently to spread out the input vectors in the order of similarity. The proposed algorithm performs remarkably well in comparison with many other algorithms for the well-known problems shown in previous papers.

• Nuclear Engineering and Technology
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• 제53권10호
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• ETRI Journal
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• 제39권3호
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• pp.398-405
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• 2017

In this paper, an artificial stereo extension method that creates stereophonic sound from a mono sound source is proposed. The proposed method first trains deep neural networks (DNNs) that model the nonlinear relationship between the dominant and residual signals of the stereo channel. In the training stage, the band-wise log spectral magnitude and unwrapped phase of both the dominant and residual signals are utilized to model the nonlinearities of each sub-band through deep architecture. From that point, stereo extension is conducted by estimating the residual signal that corresponds to the input mono channel signal with the trained DNN model in a sub-band domain. The performance of the proposed method was evaluated using a log spectral distortion (LSD) measure and multiple stimuli with a hidden reference and anchor (MUSHRA) test. The results showed that the proposed method provided a lower LSD and higher MUSHRA score than conventional methods that use hidden Markov models and DNN with full-band processing.