• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.574-579
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• 2009

There have been much difficulties to construct an optimized neural network in complex nonlinear regression problems such as selecting the networks structure and avoiding overtraining problem generated by noise. In this paper, we propose a stepwise constructive method for neural networks using a flexible incremental algorithm. When the hidden nodes are added, the flexible incremental algorithm adaptively controls the number of hidden nodes by a validation dataset for minimizing the prediction residual error. Here, the ELM (Extreme Learning Machine) was used for fast training. The proposed neural network can be an universal approximator without user intervene in the training process, but also it has faster training and smaller number of hidden nodes. From the experimental results with various benchmark datasets, the proposed method shows better performance for real-world regression problems than previous methods.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1853-1858
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• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.153-163
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• 1999

본 연구에서는 격자패널을 통한 소음전달을 감소시키기 위하여 외부 구조적 가진으로부터 유발된 경량 패널의 진동을 능동 제어하는 기법을 기술한다. 최적 되먹임제어기와 적응 앞먹임제어기가 결합된 혼합형 제어기가 진동제어기로 사용된다. 되먹임제어기는 주파수 영역의 모델규명법에 의해 추출된 다중 입/출력 패널진동계 모델에 대하여 LQG 최적기법을 이용하여 감쇠능을 향상시키도록 설계된다. 앞먹임제어기는 되먹임 궤환의 결합효과를 자동적으로 보정할 수 있는 제안된 학습법칙에 기초하여 패널의 잔류진동이 최소가 되도록 적응된다. 45.7${\times}$45.7${\times}$2.54 ${cm^3}$ 벌집형상의 고강도 패널, 4개의 관성형 구동기 및 이산신호처리장치에 의해 구현된 패널 진동계에 대한 능동제어 실험을 수행해 본 결과 600Hz 주파수대역에 대한 12dB 진동저감이 이루어 질 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.7A
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• pp.978-985
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• 2000

Blind equalizers recover the transmitted data using statistical characteristics of the signal alone. Among many alternatives, steepest gradient descent type algorithms such as the CMA and Sato algorithm are most widely utilized in practice. In this paper we propose a dual-mode NCMA algorithm, which combines the advantages of the dual mode CMA and Normalized CMA (NCMA) with the dual mode phase recovery algorithm. In addition, we perform computer simulations to demonstrate the performance improvement of the proposed algorithm with a QAM system. Simulation results show that the presented algorithm has a faster convergence speed and smaller steady-state residual error than the CMA and dual-mode CMA.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.67-74
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• 2021

As IoT(Internet Of Things) devices like a smart sensor have constrained power sources, a power strategy is critical in WSN(Wireless Sensor Networks). Therefore, it is necessary to figure out the residual power of each sensor node for managing power strategies in WSN, which, however, requires additional data transmission, leading to more power consumption. In this paper, a residual power estimation method was proposed, which uses ignorantly small amount of power consumption in the resource-constrained wireless networks including WSN. A residual power prediction is possible with the least data transmission by using Machine Learning method with some training data in this proposal. The performance of the proposed scheme was evaluated by machine learning method, simulation, and analysis.

• Environmental and Resource Economics Review
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• v.17 no.3
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• pp.135-166
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• 2008

This paper reports the empirical results of comparing the willingness to pay(WTP) for health risk reductions and the willingness to accept(WTA) for risk increases using experimental market methods in the first time in Korea. Health risks were defined as probabilities of premature death from exposure to one of As, Pb, and THM in tap water. A total of six experimental markets with 15 participants in each experiments were held using 20 repetitive Vickrey second-price sealed-bid auctions. To compare the effects of market experiences, trading a marketed good, candy bar, was introduced before the trading the non-marketed good, drinking water risks. Moreover, an objective risk information was provided after the first 10 trials to incorporate learning processes. Regardless of marketed or non-marketed goods, the mean of WTA exceeded the mean of WTP at the first auction trial. As experimental trials proceeded, the disparity between WTA and WTP for marketed goods disappeared. However results for non-marketed goods were rather mixed to the extent that WTA for health risks from As (relatively high risk leves) were significantly larger than WTP, while there were no significant difference between WTA and WTP for health risks fro Pb and THM (relatively low risk levels). On the other hand, participants seemed to respond in a 'rational' manner to the objective risk information provided, with positive learning effects of market-like experience(especially in the WTA experiments).

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.53-53
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• 1999

전계방출을 이용한 평판 표시장치는 CRT가 가진 장점을 모두 갖는 동시에 얇고 가벼우며 낮은 전력소모로 완벽한 색을 구현할 수 있는 차세대 표시장치로서 이에 대한 여국가 활발히 이루어지고 있다. 여기에 사용되는 음극물질로서 실리콘이나 몰리 등을 팁모양으로 제작하여 사용해 왔다. 하지만 잔류가스에 의한 역스퍼터링이나 화학적 반응에 의해서 전계방출 성능이 점차 저하되는 등의 해결해야할 많은 문제가 있다. 이러한 문제들을 해결하기 위하여 탄소계 재료로서 다이아몬드, 다이아몬드상 카본 등을 이용하려는 노력이 진행되어 왔다. 이중 유리화 비정형 탄소는 다량의 결함을 가지고 있는 유리질의 고상 탄소 재로로서, 전기전도도가 우수하면서 outgassing이 적고 기계적 강도가 뛰어나며 고온에서도 화학적으로 안정하여 전계방출 소자의 음극재료로서 알맞은 것으로 생각된다. 유리화 비정형 탄소가루를 전기영동법으로 기판에 코팅하여 전계방출 소자를 제작하였다. 전기영동 용액으로 이소프로필알코올에 질산마그네슘과 소량의 증류수, 유리화 비정형 탄소분말을 섞어주었고 기판으로는 몰리(Mo)가 증착된 유리를 사용하였다. 균일한 증착을 위해서 증착후 역전압을 걸어 주는 방법과 증착 후 플라즈마 처리를 하는 등의 여러 가지 방법을 사용했다. 전계방출 전류는 1$\times$10-7Torr이사에서 측정하였다. 1회 제작된 용액으로 반복해서 증착한 횟수에 따라 표면의 거치기, 입자의 분포, 전계방출 측정 결과 등의 차이가 관찰되었다. 발광이미지는 전압에 따라 변화하였고, 균일한 발광을 관찰하기 위해서 오랜 시간동안 aging 과정을 거쳐야 했다. 그리고 구 모양의 양극을 사용해서 위치를 변화시키며 시동 전기장을 관찰하여 위치에 따른 전계방출의 차이를 조사하여 발광의 균일성을 알 수 있었다.on microscopy로 분석하였으며 구조 분석은 X-선 회절분석, X-ray photoelectron spectroscopy 그리고Auger electron spectroscope로 하였다. 증착된 산화바나듐 박막의 전기화학적 특성을 분석하기 위하여 리튬 메탈을 anode로 하고 EC:DMC=1:1, 1M LiPF6 액체 전해질을 사용한 Half-Cell를 구성하여 200회 이상의 정전류 충 방전 시험을 행하였다. Half-Cell test 결과 박막의 결정성과 표면상태에 따라 매우 다른 전지 특성을 나타내었다.도상승율을 갖는 경우가 다른 베이킹 시나리오 모델에 비해 효과적이라 생각되며 초대 필요 공급열량은 200kW 정도로 산출되었다. 실질적인 수치를 얻기 위해 보다 고차원 모델로의 해석이 필요하리라 생각된다. 끝으로 장기적인 관점에서 KSTAR 장치의 베이킹 계획도 살펴본다.습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실증사례 분석결과는 일일 환율예측문제를 적용하였을 경우, 기존의 방법론보다 더 나운 예측성과를 타나내었다.pective" to workflow architectural discussions. The vocabulary suggested

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.57-62
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• 2014

This paper proposes the improving the equalization performance using the adaptive modulus concept to the MCMA blind equalizer in order to the reduction of intersymbol interference which occurs in the band limited and time dispersive communication channel. In MCMA blind algorithm, it is possible to reducing the amplitude and phase rotation of intersymbol interference without training sequence, the fixed constant modulus of transmission signal is used. But in proposed algorithm, the modulus are adaptively varies according to the equalizer output signal, then the improved equalization performance were obtained by the computer simulation. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve were used. The propose algorithm has fairly good performance compared to the traditional MCMA algorithm in the same adaptive equalization algorithm.