The secondary growth model for Salmonella was developed based on the artificial neural network (ANN) with data collected from ComBase and FoodData Central. In addition to the existing secondary model variables (temperature, pH, Na+, and water contents), more input variables (sugar, carbohydrate, lipid, and protein contents) were considered. The output variables were microbial growth parameters (lag phase duration [l] and maximum growth rate [mmax]). A commercial ANN program (NeuralWorks Predict) was utilized with training at 80%, validation at 10%, and test data at 10%. ANN models were created using all data and cleansed data. Using the cleansed data, the training/testing root mean square error (RMSE) for mmax improved from 0.14/0.16 to 0.11/0.14, whereas the RMSE for l was still not acceptable, from 11.94/33.03 to 7.09/4.18. The l data were divided into two ranges with high and low goodness of fit, whereas the ANN model for each field was built, resulting in an optimally low RMSE.

This study deals with the application of an artificial neural network (ANN) model to predict power consumption for utilizing seawater source heat pumps of recirculating aquaculture system. An integrated dynamic simulation model was constructed using the TRNSYS program to obtain input and output data for the ANN model to predict the power consumption of the recirculating aquaculture system with a heat pump system. Data obtained from the TRNSYS program were analyzed using linear regression, and converted into optimal data necessary for the ANN model through normalization. To optimize the ANN-based power consumption prediction model, the hyper parameters of ANN were determined using the Bayesian optimization. ANN simulation results showed that ANN models with optimized hyper parameters exhibited acceptably high predictive accuracy conforming to ASHRAE standards.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

PURPOSES : This study aims to determine whether machine learning techniques based on the results of chemical analysis experiments can be rationally applied to evaluate the aging of various asphalt binders used throughout the country. METHODS : We conducted chemical experiments such as FT-IR, H-NMR, C- NMR, and GPC for the three-stage aging levels of eight types of asphalt binders used in the country and utilized two artificial neural network models to determine valid chemical experimentation and conditions for the use of neural modeling through predictions. RESULTS : The M-prop model, which combined the findings from each neural network model into a single artificial neural network model, demonstrated superior predictive performance compared with the M-base model, which assessed aging using two cluster layers. In addition, the minimum amount of data required to achieve 100% predictive accuracy for the target asphalt binders, regardless of the artificial neural network model, was 18, and the amount of training data decreased to less than 50%. CONCLUSIONS : The predictive accuracy of the aging of asphalt binders was significantly enhanced when GPC data was used, indicating that GPC should be prioritized in evaluating the aging of asphalt binders.

The car market is a high-involvement, high-information market, in which consumers are expected to go through extensive searches. Cars are highly symbolic artefacts. The marque and model say a lot about the owner, and evidently, a car is far beyond a purely rational, functionally based purchase. However, car manufacturers face a serious problem as worldwide marque loyalty levels, from purchase to purchase, average below 50%, and tend to decline over time. Evidently, the analysis of factors affecting car marque loyalty is a research topic of significant managerial importance. This study attempts to empirically address the structure of marque loyalty in the car market and has a dual objective: First, to relate marque loyalty to a set of consumer characteristics under a theoretical framework, and second, to examine the impact of current car’s attribute-level performance on loyalty. In this direction, this study illustrates the value of Adaptive Network-based Fuzzy Inference System (ANFIS), as a bridge between qualitative and quantitative approaches, in an attempt to identify alternative complex antecedent conditions that give rise to marque loyalty in the car market. The proposed approach offers to conventional correlational quantitative approaches three benefits: (1) asymmetry (i.e., relationships between independent and dependent variables are treated as non-linear/asymmetric), (2) equifinality (i.e., multiple pathways may lead to the same outcome), and (3) causal complexity (i.e., combinations of antecedent conditions lead to the outcome, and hence, the focus is not on net-effects, but on combinatorial-synergistic effects). To demonstrate these merits, ANFIS is compared to a conventional econometric forecasting technique, namely logistic regression.

Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

본 논문에서는 수학적 구조 모델과 인공신경망 기법을 상호 유기적으로 결합하여 구조물의 거동 데이터로부터 부재모델 또는 재료모델의 정확도를 높이는 정보기반 하이브리드 모델 업데이트 기법을 개발하였다. 유한요소와 같은 수학적 모델을 사용하여 구조물의 거동을 모사하기 위해서는 재료, 부재, 그리고 시스템의 정확한 모델링이 우선하여야 한다. 그러나 재 료, 부재의 각 레벨에서의 수학적인 모델은 이상화과정을 거치면서 중요한 특성을 생략하거나, 시스템 구성시 부재간의 상 호작용이나 경계조건의 단순화로 인해 유한요소 모델은 실제 구조물의 거동과 차이를 보이게 된다. 본 논문에서 제시된 하 이브리드 모델 업데이트 기법은 구조물의 거동과 수학적 모델의 해석결과 차이를 인공신경망 기법을 사용하여 보완함으로 써 시스템 모델의 정확도를 높일 수 있다. 이때 시스템의 거동 데이터로부터 부재 또는 재료모델을 개선할 수 있는 데이터 를 추출하여 부재 또는 재료모델을 개선한다. 제시된 기법은 보-기둥 접합부의 이력모델을 개선하는 것으로 검증하였으며, 복잡한 거동을 보이는 시스템 모델링에 광범위하게 사용될 수 있다.

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

철근콘크리트 부재의 전단거동에 대한 오랜 연구에 의하여 이에 대한 다양한 이론모델들과 제안식들이 존재한다. 그러나 전판거동의 메커니즘이 복잡하고 영향을 미치는 요소들이 많아서 이론모델들은 대부분 매우 복잡한 경향이 있고, 실험에 의한 제안식들은 제한된 범위내의 실험변수에 대해서만 유효한 경우가 많다. 이러한 문제점을 해결할 수 있는 대안의 하나로써 인공신경망이 여러 연구자들에 의하여 제안되어 왔으며, 본 논문에서는 인공신경망을 이용하여 전단보강근이 없는 철근콘크리트 보의 전단강토를 예측하였다 특히, 기존의 전단실험결과를 광범위하게 모아 구축한 데이타베이스를 활용함으로써 넓은 범위의 구조변수들을 포함한 다양한 부재들을 인공신경망의 훈련자료로 이용하였고, 인공신경망에 의한 전단강토 예측 결과를 ACI의 규준식, Zsutty, Okamura의 제안식들과도 비교 분석하였다. ACI의 규준식은 전단보강근이 없는 철근콘크리트 부재에 대해서 매우 부정확한 전단강도를 제공하였으며, Zsutty의 제안식은 ACI의 규준식에 비해 향상된 예측 결과를 보였으나 부재의 크기효과를 반영하지 못하였다. Okamura의 제안식은 주요 변수들의 영향을 비교적 잘 반영하여 상당히 정확하면서도 안정적인 전단강토를 제공하였다 이에 비해 인공신경망은 실험 결과에 가장 근접한 부재의 전단강도를 제공함으로써, 다양한 변수들의 영향을 매우 정확하게 반영할 수 있는 것으로 나타나서 인공신경망이 전단강도와 같이 메커니즘이 복잡하고 영향을 끼치는 변수들이 많은 다른 구조적 거동이나 강도를 예측하는데 매우 적절한 수단을 제공할 수 있음을 보여주었다.통합에 사용될 수 있음을 보였다. 구현하였다. 분포면적은 최근 25년간 총 2,893ha에 이르는 얕은 습지의 매립으로 인해 크게 변화하고 있으며 하구를 찾는 수금류의 분포, 환경수용력 등과 크게 상관성이 있어 앞으로 심도 있는 연구가 더욱 필요하다.에서 단정도실수 및 배정도실수의 역수 제곱근 계산에 필요한 평균 곱셈 횟수를 계산한다 이들 평균 곱셈 횟수를 종래 알고리즘과 비교하여 본 논문에서 제안한 알고리즘의 우수성을 증명한다. 본 논문에서 제안한 알고리즘은 오차가 일정한 값보다 작아질 때까지만 반복하므로 역수 제곱근 계산기의 성능을 높일 수 있다. 또한 최적의 근사 역수 제곱근 테이블을 구성할 수 있다. 본 논문의 연구 결과는 디지털 신호처리, 컴퓨터 그라픽스, 멀티미디어, 과학 기술 연산 등 부동소수점 계산기가 사용되는 분야에서 폭 넓게 사용될 수 있다.순으로 좋게 평가되었다. 결론적으로 감농축액의 첨가는 당과 탄닌성분을 함유함으로써 인절미의 노화를 지연시키고 저장성을 높이는데 효과가 있는 것으로 생각된다. 또한 인절미를 제조할 때 찹쌀가루에의 감농축액을 첨가하는 것이 감인절미의 색, 향, 단맛, 씹힘성이 적당하고 쓴맛과 떫은맛은 약하게 느끼면서 촉촉한 정도와 부드러운 정도는 강하게 느낄수 있어서 전반적인 기호도에서 가장 적절한 방법으로 사료된다.비위생 점수가 유의적으로 높은 점수를 나타내었다. 조리종사자의 위생지식 점수와 위생관리 수행수준의 상관관계를 조사한 결과, 위생지식

이 논문에서는 기존의 보-거더 구조계 주차장 구조물에 대한 차량하중영향 연구를 토대로, 플랫 슬래브 구조계에서 차량하중영향에 대한 연구를 수행하였다. 먼저, 최대부재력을 일으키는 차량하중의 적용을 위해 플랫 슬래브의 주요 설계지점에 대한 영향면을 구성하였으며, 플랫 슬래브의 등가차량하중계수를 인공 신경망기법을 이용하여, 슬래브 두께, 지판 두께, 지판 크기, 슬래브의 단변, 장변 길이 등 주요구조변수로 제시하였다. 사용된 신경망의 훈련은 많은 패턴수를 갖는 비선형 회귀분석에 적합한 Levenberg-Marquardt 알고리즘을 이용하였으며 해석결과와 인공 신경망의 출력의 비교를 통해 알고리즘의 유효성을 검증하였다. 플랫 슬래브 구조계의 등가차량하중계수를 살펴보면, 보-거더 구조계의 경우와 유사하게 주열대와 중간대의 정모멘트 부재력에서 차량하중에 매우 취약함을 알 수 있었다.

인공 신경회로망은 인간의 뇌를 전산 모델로 구현한 것으로 상호 연결된 많은 정보 처리 유니트들로 구성되어 있으며, 이를 기초로 논리적인 추론을 수행할 수 있다. 특히, 신경망은 비선형 변수를 많이 포함하고 있는 복잡한 문제 해결에서 더욱 효과적이다. 신경망의 이러한 기능으로 인해 구조분야에서는 비선형적인 각종 구조실험의 결과예측이나 구조계획 그리고 최적 설계에 응용되고 있는 추세이다. 본 논문에서는 인공 신경 회로망의 기본 이론을 설명하고, 현재까지 정립되고 있지 않은 대형 콘크리트 판넬간 수직 접합부의 최대 전단 내력 예측에 기존의 제안식과 인공 신경 회로망의 예측 결과를 비교하여 신경망의 적용가능성을 검토하고자 한다.

This study suggests the yield forecast model for chilli pepper using artificial neural network. For this, we select the most suitable network models for chilli pepper’s yield and compare the predictive power with adaptive expectation model and panel model. The results show that the predictive power of artificial neural network with 5 weather input variables (temperature, precipitation, temperature range, humidity, sunshine amount) is higher than the alternative models. Implications for forecasting of yields are suggested at the end of this study.

The core decisions of bulk shipping businesses can be summarized as the timing and the choice of period for which carrying capacity is traded. In particular, frequent decisions to trade freight either with repeated spot transactions or with a one-off long-term deal critically impact business performance. Even though a variety of freight trading strategies can be employed to facilitate the decisions, chartering practitioners have not been active in utilizing these strategies, and academic research has rarely proposed applicable solutions. The specific properties of freight as a tradable commodity are not properly reflected in existing studies, and limitations have been reported in their application to the real world. This research focused on the establishment of applicable freight trading strategies by taking into account two properties of freight: time perishability and term-dependant pricing. In addition to traditional trading strategies, artificial neural networks were applied for the first time to the test of freight trading strategies. The performances of the trading strategies were measured and compared to produce a remarkable outperformance of the ANN. This research is expected to make a significant contribution to chartering practices by enhancing the quality of chartering decisions and eventually enabling the effective management of freight rate risk. In addition to methodological expansion, the result will propose a way to approach the controversial issue of freight market efficiency.