• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.443-451
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• 2005

The studied in this paper is a new algorithm for searching the maximum likelihood estimate(MLE) in which probability density function is not explicitly expressed. Newton-Raphson's root-finding routine and a nonlinear numerical optimization algorithm with constraint (so-called feasible sequential quadratic programming) are used. This algorithm is applied to the Wakeby distribution which is importantly used in hydrology and water resource research for analysis of extreme rainfall. The performance comparison between maximum likelihood estimates and method of L-moment estimates (L-ME) is studied by Monte-carlo simulation. The recommended methods are L-ME for up to 300 observations and MLE for over the sample size, respectively. Methods for speeding up the algorithm and for computing variances of estimates are discussed.

• 한국토양비료학회지
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• 제49권5호
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• pp.461-468
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• 2016

One of the important phytohormones produced by plant growth promoting bacteria is the auxin; indole-3-acetic acid (IAA), with L-tryptophan as the precursor. In this study, we focused on the investigation of optimal conditions for the production of IAA by Bacillus megaterium BM5. We investigated culturing conditions, such as incubation temperature, pH of the culture medium and incubation period, with varying media components such as inoculation volume, tryptophan concentration and carbon and nitrogen source. Besides, optimization study intended for high IAA production was carried out with fermentation parameters such as rpm and aeration. The initial yield of $42{\mu}g\;IAA\;ml^{-1}$ after 24 hr increased to $85{\mu}g\;ml^{-1}$ when 5% (v/v) of L-tryptophan was used in the culture broth. The maximum yield of $320{\mu}g\;IAA\;ml^{-1}$ was observed in trypticase soy broth (TSB) supplemented with starch and soybean meal as C and N sources with a C/N ratio of 3:1 (v/v) at $30^{\circ}C$, pH 8.0 for 48 hrs with 1.0 vvm and 250 rpm in 5 L working volume using 10 L scale fermenter. The bacterial auxin extracted from the culture broth was confirmed by thin layer chromatography and high-performance liquid chromatography and effect on plant growth was confirmed by root elongation test.

• Journal of Power Electronics
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• 제18권1호
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• pp.45-55
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• 2018

It is usually difficult for dual-active-bridge (DAB) dc-dc converters to operate efficiently at light loads. This paper presents an in-depth analysis of a DAB with triple-phase-shift (TPS) control under the light load condition to overcome this problem. A kind of operating mode which is suitable for light load operation is analyzed in this paper. First, an analysis of the zero-voltage-switching (ZVS) constraints for the DAB converter has been carried out and a reasonable dead-band setting method has been proposed. Secondly, the basic operating characteristics of the converter are analyzed. Third, under the condition of satisfying the ZVS constraints, both the reactive power and the root mean square (RMS) value of the current are simultaneously minimized and a particle swarm optimization (PSO) algorithm is employed to analyze and solve this optimization problem. Lastly, both simulations and experiments are carried out to verify the effectiveness of the proposed method. The experimental results show that the converter can effectively achieve ZVS and improved efficiency.

• Steel and Composite Structures
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• 제39권4호
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• pp.471-491
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• 2021

In this study, we estimate the ultimate load of rectangular concrete-filled steel tubes (CFST) by developing a novel hybrid predictive model (ANN-BCMO) which is a combination of balancing composite motion optimization (BCMO) - a very new optimization technique and artificial neural network (ANN). For this aim, an experimental database consisting of 422 datasets is used for the development and validation of the ANN-BCMO model. Variables in the database are related with the geometrical characteristics of the structural members, and the mechanical properties of the constituent materials (steel and concrete). Validation of the hybrid ANN-BCMO model is carried out by applying standard statistical criteria such as root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). In addition, the selection of appropriate values for parameters of the hybrid ANN-BCMO is conducted and its robustness is evaluated and compared with the conventional ANN techniques. The results reveal that the new hybrid ANN-BCMO model is a promising tool for prediction of the ultimate load of rectangular CFST, and prove the effective role of BCMO as a powerful algorithm in optimizing and improving the capability of the ANN predictor.

• Smart Structures and Systems
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• 제29권4호
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• pp.523-533
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• 2022

A novel metaheuristic search method, namely black widow optimization (BWO) is employed to increase the accuracy of slope stability analysis. The BWO is a recently-developed optimizer that supervises the training of an artificial neural network (ANN) for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The designed slope bears a loaded foundation in different distances from the crest. A sensitivity analysis is conducted based on the number of active individuals in the BWO algorithm, and it was shown that the best performance is acquired for the population size of 40. Evaluation of the results revealed that the capability of the ANN was significantly enhanced by applying the BWO. In this sense, the learning root mean square error fell down by 23.34%. Also, the correlation between the testing data rose from 0.9573 to 0.9737. Therefore, the postposed BWO-ANN can be promisingly used for the early prediction of FOS in real-world projects.

• 한국미생물·생명공학회지
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• 제51권1호
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• pp.69-82
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• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• Smart Structures and Systems
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• 제29권5호
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• pp.677-691
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• 2022

Water cycle algorithm (WCA) has been a very effective optimization technique for complex engineering problems. This study employs the WCA for simultaneous prediction of heating load (L_H) and cooling load (L_C) in residential buildings. This algorithm is responsible for optimally tuning a neural network (NN). Utilizing 614 records, the behavior of the L_H and L_C is explored and the captured knowledge is then used to predict for 154 unanalyzed building conditions. Since the WCA is a population-based algorithm, different numbers of the searching agents were tested to find the most optimum configuration. It was observed that the best solution is discovered by 500 agents. A comparison with five newly-developed benchmark optimizers, namely equilibrium optimizer (EO), multi-tracker optimization algorithm (MTOA), slime mould algorithm (SMA), multi-verse optimizer (MVO), and electromagnetic field optimization (EFO) revealed that the WCANN predicts the desired parameters with considerably larger accuracy. Obtained root mean square errors (1.4866, 2.1296, 2.8279, 2.5727, 2.5337, and 2.3029 for the L_H and 2.1767, 2.6459, 3.1821, 2.9732, 2.9616, and 2.6890 for the L_C) indicated that the most reliable prediction was presented by the proposed model. The EFONN, however, provided a more time-effective solution. Lastly, an explicit predictive formula was elicited from the WCANN.

• Corrosion Science and Technology
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• 제23권2호
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• pp.131-138
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• 2024

This paper introduces Primetals Technologies' Through-Process Optimization (TPO) Services and Through-Process Quality Control (TPQC) System, which integrate domain knowledge, software, and automation expertise to assist steel producers in achieving operational excellence. TPQC collects high-resolution process and product data from the entire production route, providing visualizations and facilitating quality assurance. It also enables the application of artificial intelligence techniques to optimize processes, accelerate steel grade development, and enhance product quality. The main objective of TPO is to grow and digitize operational know-how, increase profitability, and better meet customer needs. The paper describes the contribution of these systems to achieving operational excellence, with a focus on quality assurance. Transparent and traceable production data is used for manual and automatic quality evaluation, resulting in product quality status and guiding the product disposition process. Deviation management is supported by rule-based and AI-based assistants, along with monitoring, alarming, and reporting functions ensuring early recognition of deviations. Embedded root cause proposals and their corrective and compensatory actions facilitate decision support to maintain product quality. Quality indicators and predictive quality models further enhance the efficiency of the quality assurance process. Utilizing the quality assurance software package, TPQC acts as a "one-truth" platform for product quality key players.

• 한국식물생명공학회:학술대회논문집
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• 한국식물생명공학회 2002년도 춘계학술대회
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• pp.150-150
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• 2002

• Restorative Dentistry and Endodontics
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• 제42권2호
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• pp.105-110
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• 2017

Objectives: This study investigated the cleaning ability of ultrasonically activated irrigation (UAI) and a novel activation system with reciprocating motion (EC, EasyClean, Easy Equipamentos $Odontol\acute{o}gicos$) when used with a relatively new chelating agent (QMix, Dentsply). In addition, the effect of QMix solution when used for a shorter (1 minute) and a longer application time (3 minutes) was investigated. Materials and Methods: Fifty permanent human teeth were prepared with K3 rotary system and 6% sodium hypochlorite. Samples were randomly assigned to five groups (n = 10) according to the final irrigation protocol: G1, negative control (distilled water); G2, positive control (QMix 1 minute); G3, QMix 1 minute/UAI; G4, QMix 1 minute/EC; G5, QMix 3 minutes. Subsequently the teeth were prepared and three photomicrographs were obtained in each root third of root walls, by scanning electron microscopy. Two blinded and pre-calibrated examiners evaluated the images using a four-category scoring system. Data were statistically analyzed using Kruskal-Wallis and Dunn tests (p < 0.05). Results: There were differences among groups (p < 0.05). UAI showed better cleaning ability than EC (p < 0.05). There were improvements when QMix was used with auxiliary devices in comparison with conventional irrigation (p < 0.05). Conventional irrigation for 3 minutes presented significantly better results than its use for 1 minute (p < 0.05). Conclusions: QMix should be used for 1 minute when it is used with UAI, since this final irrigation protocol showed the best performance and also allowed clinical optimization of this procedure.