• 대한의용생체공학회:의공학회지
• /
• 제43권1호
• /
• pp.19-26
• /
• 2022

The Comprehensive in vitro Proarrhythmic Assay(CiPA) project was launched for solving the hERG assay problem of being classified as high-risk groups even though they are low-risk drugs due to their high sensitivity. CiPA presented a protocol to predict drug toxicity using physiological data calculated based on the in-silico model. in this study, features calculated through the in-silico model are analyzed for correlation of changing action potential in the near future, and features are verified through predictive performance according to drug datasets. Using the O'Hara Rudy model modified by Dutta et al., Pearson correlation analysis was performed between 13 features(dVm/dtmax, APpeak, APresting, APD90, APD50, APDtri, Ca_peak, Ca_resting, CaD90, CaD50, CaDtri, qNet, qInward) calculated at 100 pacing, and between dVm/d_{tmax_repol} calculated at 1,000 pacing, and linear regression analysis was performed on each of the 12 training drugs, 16 verification drugs, and 28 drugs. Indicators showing high coefficient of determination(R²) in the training drug dataset were qNet 0.93, AP resting 0.83, APDtri 0.78, Ca resting 0.76, dVm/dt_max 0.63, and APD90 0.61. The indicators showing high determinants in the validated drug dataset were APDtri 0.94, APD90 0.92, APD50 0.85, CaD50 0.84, qNet 0.76, and CaD90 0.64. Indicators with high coefficients of determination for all 28 drugs are qNet 0.78, APD90 0.74, and qInward 0.59. The indicators vary in predictive performance depending on the drug dataset, and qNet showed the same high performance of 0.7 or more on the training drug dataset, the verified drug dataset, and the entire drug dataset.

• 지능정보연구
• /
• 제17권4호
• /
• pp.95-108
• /
• 2011

인터넷 비즈니스의 활성화에 따라서 기업은 고객의 인물정보 및 거래정보를 활용하여 보다 맞춤화된 개인화 서비스를 제공하고 있다. 기존의 고객군별 예측기법은 유사한 고객들을 군집화하여 고객군별로 예측모델을 수립하는 것으로, 구매가 많고 충성도가 높은 핵심고객에게 요구되는 일대일 서비스를 제공하는 데는 한계가 있다. 반면 일대일 고객별 예측기법은 각 고객에게 고도로 맞춤화된 서비스를 제공하지만, 과거 구매이력이 많지 않은 고객 이나 신규 고객에게는 정확한 개인화 서비스를 제공하지 못한다. 본 연구는 고객의 구매빈도에 따라서 유사 고객들과의 군집화 수준을 동적으로 조정하는 새로운 지능형 개인화 시스템을 제안한다. 제안된 시스템은 과거 구매가 많은 고객들에 대해서는 일대일 예측모델을 수립하지만, 구매 빈도가 낮은 고객의 경우 다른 고객들과의 최적화된 군집화를 통해 예측모델을 수립한다. 본 기법을 Neilsen의 음료수 구매 데이터셋에 적용하여 고객의 일회 구매금액 및 구매품목을 예측한 결과, 기존 두 예측기법들에 비하여 적정한 계산비용(computational cost)으로 더욱 정확한 개안화 서비스를 제공할 수 있음을 확인하였다.

• 한국농업기계학회:학술대회논문집
• /
• 한국농업기계학회 2017년도 춘계공동학술대회
• /
• pp.45-45
• /
• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Bulletin of the Korean Chemical Society
• /
• 제26권12호
• /
• pp.2007-2016
• /
• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.1659-1663
• /
• 1991

A new control design methodology is presented here which is based on a nonlinear time-series reference model. It is indicated by highly nonlinear simulations that such designs successfully stabilize troublesome aircraft maneuvers undergoing large changes in angle of attack as well as large electric power transients due to line faults. In both applications, the nonlinear controller was significantly better than the corresponding linear adaptive controller. For the electric power network, a flexible a.c. transmission system (FACTS) with series capacitor power feedback control is studied. A bilinear auto-regressive moving average (BARMA) reference model is identified from system data and the feedback control manipulated according to a desired reference state. The control is optimized according to a predictive one-step quadratic performance index (J). A similar algorithm is derived for control of rapid changes in aircraft angle of attack over a normally unstable flight regime. In the latter case, however, a generalization of a bilinear time-series model reference includes quadratic and cubic terms in angle of attack. These applications are typical of the numerous plants for which nonlinear adaptive control has the potential to provide significant performance improvements. For aircraft control, significant maneuverability gains can provide safer transportation under large windshear disturbances as well as tactical advantages. For FACTS, there is the potential for significant increase in admissible electric power transmission over available transmission lines along with energy conservation. Electric power systems are inherently nonlinear for significant transient variations from synchronism such as may result for large fault disturbances. In such cases, traditional linear controllers may not stabilize the swing (in rotor angle) without inefficient energy wasting strategies to shed loads, etc. Fortunately, the advent of power electronics (e.g., high-speed thyristors) admits the possibility of adaptive control by means of FACTS. Line admittance manipulation seems to be an effective means to achieve stabilization and high efficiency for such FACTS. This results in parametric (or multiplicative) control of a highly nonlinear plant.

• 한국건설관리학회논문집
• /
• 제5권6호
• /
• pp.138-145
• /
• 2004

공사에 소요되는 기간은 시설물의 품질과 비용에 직접적인 영향을 미치지만, 고등학교 시설공사의 경우 경험과 직관에 의거하여 공기를 산정하고 있어 공사수행과정에서 계약당사자 간에 분쟁이 많이 발생하고 있다. 본 논문은 고등학교 시설공사에 소요되는 기간의 산정에 영향을 미치는 다양한 요인을 분석하여 공기 산정기준을 제안하는 것으로, 인천지 역에서 최근에 개교한 고등학교의 실적자료를 수집하여 다중선형 회귀분석 하였다. 회귀분석 결과로 얻은 순 공사기간에 인천지 역의 기후특성을 고려하여 산정한 작업불가능기간을 더하여 총 공사기간을 산출 하였다. 본 논문에서 제안한 공기 산정식은 공사발주 및 계약 시 계약공기를 정확하게 산정 하는데 도움을 줄 수 있을 것이다

• 한국화재소방학회논문지
• /
• 제28권4호
• /
• pp.97-103
• /
• 2014

소방보호복은 고열유속에 의한 화상방지를 위해 3층 이상의 복합소재로 구성되어 있으며, 각 소재 사이는 공기 간극이 존재한다. 화재에 의한 고열유속 노출 시 공기 간극 내에서의 열전달은 대류와 복사에 의해 주로 발생하며, 그로 인해 간극의 크기에 따라서 비선형 특징의 열 저항 크기를 갖게 된다. 그러므로 본 연구에서는 보호복 소재 사이의 여러 가지 공기 간극(0~7 mm)에 대한 보호복의 열 보호성능을 자세히 파악하기 위한 실험을 수행하였다. 복사 열 유속 입사시에 시간에 따른 각 소재의 온도 변화뿐만 아니라, 열 보호성능을 가장 효과적으로 나타낼 수 있는 지표(Radiant Protective Performance, RPP) 값의 공기간극에 대한 변화 특성을 파악하였다. 공기간극이 증가할수록 단열효과가 커짐으로 인해 후면의 온도는 낮아지고, RPP는 커짐을 확인할 수 있었다. 특히 일정 열유속 조건에서 공기간극에 대한 RPP 값은 선형적인 특성을 나타내었고, 그러한 결과를 바탕으로 다양한 입사 열유속 및 공기 간극 조건에 대해 비교적 간단한 형태의 RPP 지표 예측 식을 제안하였고, 좋은 예측 결과를 얻을 수 있었다.

• International Journal of Control, Automation, and Systems
• /
• 제1권2호
• /
• pp.194-202
• /
• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Asian Pacific Journal of Cancer Prevention
• /
• 제16권17호
• /
• pp.7721-7725
• /
• 2015

Background: The aim of this study was to assess the predictive role of religious coping in quality of life of breast cancer patients. Materials and Methods: This multi-center cross-sectional study was conducted in Tehran, Iran, from October 2014 to May 2015. A total of 224 women with breast cancer completed measures of socio-demographic information, religious coping (brief RCOPE), and quality of life (FACT-B). Data were analyzed using descriptive statistics and the t-test, ANOVA, and linear regression analysis. Results: The mean age was 47.1 (SD=9.07) years and the majority were married (81.3%). The mean score for positive religious coping was 22.98 (SD=4.09) while it was 10.13 (SD=3.90) for negative religious coping. Multiple linear regression showed positive and negative religious coping as predictor variables explained a significant amount of variance in overall QOL score ($R^2=.22$, P=.001) after controlling for socio-demographic, and clinical variables. Positive religious coping was associated with improved QOL (${\beta}=0.29$; p=0.001). In contrast, negative religious coping was significantly associated with worse QOL (${\beta}=-0.26$; p=0.005). Conclusions: The results indicated the used types of religious coping strategies are related to better or poorer QOL and highlight the importance of religious support in breast cancer care.

• 말소리와 음성과학
• /
• 제6권3호
• /
• pp.63-72
• /
• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.