• 한국측량학회지
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• 제34권5호
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• pp.525-534
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• 2016

쌀은 오랜 기간 동안 남한 지역의 주식임과 동시에 농부들의 주 수입원이며, 농업 분야 관련 정책 수립을 위한 수학적인 쌀 생산량 추정 모델의 구축이 필요하다. 본 연구의 목적은 (1) 쌀 생산량 추정을 위한 회선신경망 모델의 구축과, (2) 최고의 성능을 보이는 회선신경망의 파라미터를 결정하는 것과, (3) 인공신경망 모델과의 비교를 통해 회선신경망의 성능을 평가하는 것이다. 각 모델의 입력데이터로는 2000~2013년도의 4~9월까지에 해당하는 기상자료와 MODIS 위성자료를 사용하였으며, 정확도 평가를 위해 교차 검증을 실시하였다. 회선신경망과 인공신경망은 쌀 생산 표본점을 대상으로 각각 36.10kg/10a, 48.61kg/10a와 시군구 지역을 대상으로 각각 31.30kg/10a, 39.31kg/10a의 RMSE를 보였다. 회선신경망 모델은 인공신경망 모델보다 우수한 성능을 보였으며, 본 연구를 통해 쌀 생산량 추정 분야에 대한 회선신경망 모델의 적용 가능성을 확인할 수 있었다.

• Structural Monitoring and Maintenance
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• 제1권3호
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.

• Korean Chemical Engineering Research
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• 제53권2호
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• pp.236-242
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• 2015

고분자전해질 연료전지 스택의 성능 및 주요 운전 변수를 예측하기 위해 부분최소자승법과 인공신경망의 두 가지 데이터 기반 모델링 기법을 제시한다. 30 kW급 고분자전해질 연료전지 스택 실험으로부터 확보한 데이터를 사용하여 부분최소자승 및 인공신경망 모델들을 구성한 후 각 모델의 예측 성능 및 계산 시간을 비교하였다. 모델의 복잡성을 줄이기 위해 부분최소자승법에 기초한 VIP(Variable Importance on PLS Projections) 선정기준을 모델링 절차에 포함하여, 초기 입력변수의 집합으로부터 모델링에 필요한 입력변수들을 선정하였다. 모델링 결과, 인공신경망이 스택의 평균 셀전압과 캐소드(cathode) 출구 온도를 예측하는데 있어서, 부분최소자승법 보다 우수한 성능을 보였다. 그러나 부분최소자승법 또한 입력변수와 출력변수 간에 선형적 상관관계만을 모델링 할 수 있음에도 불구하고 비교적 만족할 만한 예측 성능을 나타냈다. 모델의 정확도와 계산속도의 요구조건에 따라 두 모델링 기법은 고분자전해질 연료전지의 설계 및 운전 분야의 성능 예측, 온라인 및 오프라인 최적화, 제어 및 이상 진단을 위해 적용될 수 있을 것으로 판단된다.

• 대한토목학회논문집
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• 제29권2B호
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• pp.111-120
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• 2009

교각주위의 국부세굴은 교량붕괴의 주원인 중 하나로 알려져 있다. 세굴심을 산정하는 방법에는 경험식에 의한 방법과 수치모형을 이용한 시뮬레이션이 있다. 하지만 경험식에 의한 방법은 공식이 적용될 수 있는 유사한 상황에서만 제한적으로 사용가능하며, 수치모형을 이용한 방법은 비용이 많이 든다는 단점을 가지고 있다. 그러므로 본 연구에서는 세굴심 예측을 위한 CSU 공식, 다중회귀분석, 다양한 인공신경망 모형의 유용성을 비교분석하였다. 또한 세굴심을 산정하는데 있어 넓은 범위의 오차를 발생시키는 인공신경망 모형의 단점을 보완하기 위하여 본 연구에서는 인공신경망 모형에 군집분석을 결합하여 오차를 감소시키고자 하였다. 세굴심 예측을 위해 CSU 공식, 다중회귀분석, 다양한 인공신경망 모형을 적용해 본 결과 역전파알고리즘을 이용하는 인공신경망 모형이 가장 높은 정확성을 보였으며, 인공신경망 모형에 군집분석을 적용한 세굴심 예측에서는 군집수가 3일 때 가장 높은 정확도를 보였다. 군집분석을 적용한 인공신경망 모형의 정확도는 다른 모형과 비교할 때 최고 42.73%가 향상된 결과를 보여 인공신경망 모형내의 군집분석의 적용이 인공신경망의 오차를 줄이는데 큰 역할을 할 수 있음을 알 수 있었다.

• 대한기계학회논문집A
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• 제33권5호
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• pp.514-520
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• 2009

In configuring an automated polishing system, a monitoring scheme to estimate the surface roughness is necessary. In this study, a precision polishing process, magnetic abrasive finishing (MAF), along with an in-process monitoring setup was investigated. A magnetic tooling is connected to a CNC machining to polish the surface of stavax(S136) die steel workpieces. During finishing experiments, both AE signals and force signals were sampled and analysed. The finishing results show that MAF has nano scale finishing capability (upto 8nm in surface roughness) and the sensor signals have strong correlations with the parameters such as gap between the tool and workpiece, feed rate and abrasive size. In addition, the signals were utilized as the input parameters of artificial neural networks to predict generated surface roughness. Among the three networks constructed -AE rms input, force input, AE+force input- the ANN with sensor fusion (AE+force) produced most stable results. From above, it has been shown that the proposed sensor fusion scheme is appropriate for the monitoring and prediction of the nano scale precision finishing process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• Advances in environmental research
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• 제4권2호
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1478-1481
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• 2005

As the model of most practical system cannot be obtained, the practice of typical control method is limited. Accordingly, numerous artificial intelligence control methods have been used widely. Fuzzy control and neural network control have been an important point in the developing process of the field. This paper is proposed adaptive fuzzy-neural network based on the vector controlled interior permanent magnet synchronous motor drive system. The fuzzy-neural network is first utilized for the speed control. A model reference adaptive scheme is then proposed in which the adaptation mechanism is executed using fuzzy-neural network. Also, this paper is proposed estimation of speed of interior permanent magnet synchronous motor using artificial neural network controller. The back-propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back-propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the analysis results to verify the effectiveness of the new method.

• The Journal of Asian Finance, Economics and Business
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• 제7권10호
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• pp.9-21
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• 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

• 조명전기설비학회논문지
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• 제12권1호
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• pp.103-110
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• 1998

부하특성의 모델링은 부하의 비선형특성 때문에 어려운 문제이다. 이 연구는 부하특성을 표현하기 위하여 비선형 문제를 근사화 할 수 있는 신경회로망을 이용하였다. 대표적인 개별부하를 선정하고 전압과 주파수 변화에 대한 유효, 무효전력의 응답을 실험을 통해 얻었다. 그리고 개별부하특성을 식별하기 위하여 실험자료를 근거로 신경회로망을 구축하고 학습하였다. 학습된 신결회로망은 또다른 전압, 주파수 변화에 대한 개별부하의 특성을 식별하였다. 아울러 제안된 방법의 타당성을 입증하기 위하여 식별된 결과를 제시하였다.