• Structural Engineering and Mechanics
• /
• 제51권2호
• /
• pp.299-313
• /
• 2014

In the first part of this paper, the influences of some of crack parameters on natural frequencies of a cracked cantilever Functionally Graded Beam (FGB) are studied. A cantilever beam is modeled using Finite Element Method (FEM) and its natural frequencies are obtained for different conditions of cracks. Then effect of variation of depth and location of cracks on natural frequencies of FGB with single and multiple cracks are investigated. In the second part, two Multi-Layer Feed Forward (MLFF) Artificial Neural Networks (ANNs) are designed for prediction of FGB's Cracks' location and depth. Particle Swarm Optimization (PSO) and Back-Error Propagation (BEP) algorithms are applied for training ANNs. The accuracy of two training methods' results are investigated.

• 한국정밀공학회지
• /
• 제31권10호
• /
• pp.947-954
• /
• 2014

Performance of next generation susceptor is affected by temperature uniformity in order to produce reliably large-sized flat panel display. In this paper, we propose a learning estimation model of susceptor to predict and appropriately assess the temperature uniformity. Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) are compared for the suitability of the learning estimation model. It is proved that SVMs provides more suitable verification of uniformity modeling than ANNs during each stage of temperature variations. Practical procedure for uniformity estimation of susceptor temperature was developed using the SVMs prediction algorithm.

• 대한기계학회논문집A
• /
• 제34권7호
• /
• pp.813-820
• /
• 2010

포틀랜드 시멘트용 분쇄기는 독일과 일본 등 선진국에서 도입된 고가의 대형 기계이다. 따라서 이에 대한 체계적 정비 및 보수가 원활히 진행되어야 포틀랜드 시멘트의 생산설비에 대한 안정성을 확보할 수 있다. 한편 국내에 도입된 수직형 롤러 분쇄기는 포틀랜드 시멘트의 원료인 석회석의 시간당 생산량이 5.5MN이나 되는 세계 최대 규모의 분쇄기로서 설계 수명이 $4{\times}10^{7%}$사이클 정도이나 대략 $4{\times}10^6\;{\sim}\;8{\times}10^6$ 사이클 정도에서 파괴되고 있어 계획 예방 정비에 대한 어려움이 있으며, 수직형 롤러 분쇄기의 보수비용을 절감하기 위하여 롤러 분쇄기에 대한 효과적인 재설계가 필요한 실정이다. 따라서 본 연구에서는 확률론적인 절차가 내재되어 있어 불확실성을 다룰 수 있고, 대량의 복잡한 비선형적인 관계도 단순화의 과정 없이 연관 관계를 자체 조직화할 수 있는 인간의 뇌와 가장 유사한 병렬연산모델인 신경회로망을 수직형 롤러 분쇄기에 적용하여 최적설계를 수행하였다.

• 한국지반공학회:학술대회논문집
• /
• 한국지반공학회 2008년도 춘계 학술발표회 초청강연 및 논문집
• /
• pp.987-997
• /
• 2008

Successful design, construction and maintenance of geotechnical structure in soft ground and marine clay demands prediction, control, stability estimation and monitoring of settlement with high accuracy. It is important to predict and to estimate the compression index of soil for predicting of ground settlement. Lab. and field tests have been and are indispensable tools to achieve this goal. In this paper, Artificial Neural Networks (ANNs) model with Levenberg-Marquardt Algorithm and field database were used to predict compression index of soil in Korea. Based on soil property database obtained from more than 1800 consolidation tests from soils samples, the ANNs model were proposed in this study to estimate the compression index, using multiple soil properties. The compression index from the proposed ANN models including multiple soil parameters were then compared with those from the existing empirical equations.

• Computers and Concrete
• /
• 제24권4호
• /
• pp.329-345
• /
• 2019

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
• /
• pp.696-704
• /
• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• 한국정밀공학회지
• /
• 제17권6호
• /
• pp.176-184
• /
• 2000

Reverse engineering (RE) is a process to create computer aided design (CAD) models from the scanned data of an existing part acquired using 3D position scanners. This paper proposes a novel methodology of extracting geometric features directly from a set of 3D scanned points, which utilizes the concepts of feature-based technology and artificial neural networks (ANNs). The use of ANN has enabled the development of a flexible feature-based RE application that can be trained to deal with various features. The following four main tasks were mainly investigated and implemented: (1) Data reduction; (2) edge detection; (3) ANN-based feature recognition; (4) feature extraction. This approach was validated with a variety of real industrial components. The test results show that the developed feature-based RE application proved to be suitable for reconstructing prismatic features such as block, pocket, step, slot, hole, and boss, which are very common and crucial in mechanical engineering products.

• Advances in nano research
• /
• 제9권1호
• /
• pp.1-13
• /
• 2020

In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N.Del) inside layers of nano-pipe (N.P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N.Del location/size inside the N.P layers, with high accuracy, simple and low-cost. The dielectric properties of the N.P material are measured before and after N.Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N.Del, a finite element (FE) simulation model for N.Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N.Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.

• Nuclear Engineering and Technology
• /
• 제56권7호
• /
• pp.2690-2697
• /
• 2024

With advancements in machine learning technologies, artificial neural networks (ANNs) are being widely used to improve the performance of gamma-ray spectroscopy based on NaI(Tl) scintillation detectors. Typically, the performance of ANNs is evaluated using test datasets composed of actual spectra. However, the generation of such test datasets encompassing a wide range of actual spectra representing various scenarios often proves inefficient and time-consuming. Thus, instead of measuring actual spectra, we generated virtual spectra with diverse spectral features by sampling from categorical distribution functions derived from the base spectra of six radioactive isotopes: ⁵⁴Mn, ⁵⁷Co, ⁶⁰Co, ¹³⁴Cs, ¹³⁷Cs, and ²⁴¹Am. For practical applications, we determined the optimum counting time (OCT) as the point at which the change in the Kullback-Leibler divergence (ΔKLDV) values between the synthetic spectra used for training the ANN and the virtual spectra approaches zero. The accuracies of the actual spectra were significantly improved when measured up to their respective OCTs. The outcomes demonstrated that the proposed method can effectively determine the OCTs for gamma-ray spectroscopy based on ANNs without the need to measure actual spectra.

• KIEE International Transactions on Power Engineering
• /
• 제2A권4호
• /
• pp.136-144
• /
• 2002

Load models are very important for improving the accuracy of stability analysis and load flow studies. Various loads are connected to a power bus and their characteristics of power consumption change with voltage and frequency. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this work, artificial neural networks-ANNs- were used to construct the component load models for more accurate modeling. A typical residential load was selected and subjected to a test under variable voltage/frequency conditions. Acquired data were used to construct component models by ANNs. The aggregation process of separately determined load models is also presented in the paper. Furthermore, this paper proposes a method to transform a single load model constructed by the aggregation method into a mathematical load model that can be used in traditional power system analysis software.