• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Structural Engineering and Mechanics
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• v.83 no.4
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.

• Steel and Composite Structures
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• v.48 no.5
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• pp.531-545
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• 2023

Composite dowels are implemented as a powerful alternative to headed studs for the efficient combination of Ultra High-Performance Concrete (UHPC) with high-strength steel in novel composite structures. They are required to provide sufficient shear resistance and ensure the transmission of tensile forces in the composite connection in order to prevent lifting of the concrete slab. In this paper, the load bearing capacity of puzzle-shaped and clothoidal-shaped dowels encased in UHPC specimen were investigated based on validated experimental test data. Considering the influence of the embedment depth and the spacing width of shear dowels, the characteristics of UHPC square plate on the load bearing capacity of composite structure, 240 numeric models have been constructed and analyzed. Three artificial intelligence approaches have been implemented to learn the discipline from collected experimental data and then make prediction, which includes Artificial Neural Network-Particle Swarm Optimization (ANN-PSO), Adaptive Neuro-Fuzzy Inference System (ANFIS) and an Extreme Learning Machine (ELM). Among the factors, the embedment depth of composite dowel is proved to be the most influential parameter on the load bearing capacity. Furthermore, the results of the prediction models reveal that ELM is capable to achieve more accurate prediction.

• Restorative Dentistry and Endodontics
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• v.49 no.2
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• pp.16.1-16.10
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• 2024

Objectives: This cross-sectional study evaluated the prevalence of apical periodontitis (AP) and the technical quality of root canal fillings in an adult Kuwaiti subpopulation using cone-beam computed tomography (CBCT) images. Materials and Methods: Two experienced examiners analyzed 250 CBCT images obtained from Kuwaiti patients aged 15-65 years who attended government dental specialist clinics between January 2019 and September 2020. The assessment followed the radiographic scoring criteria proposed by De Moor for periapical status and the technical quality of root canal filling. Chi-square and Fisher's exact tests were used for statistical analysis, with significance level set at p < 0.05. Results: Among the 2,762 examined teeth, 191 (6.91%) exhibited radiographic signs of AP, and 176 (6.37%) had undergone root canal filling. AP prevalence in root canal-treated teeth was 32.38%, with a significant difference between males and females. Most of the endodontically treated teeth exhibited adequate root canal filling (71.5%). Conclusions: The study demonstrated a comparable prevalence of AP and satisfactory execution of root canal treatment compared to similar studies in different countries.

• Journal of Radiation Protection and Research
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• v.33 no.4
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• pp.161-166
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• 2008

In order to determine the effect of extremely low frequency electromagnetic fields (ELF-EMF) on the frequency of micronuclei (MN), aneuploidy and chromosomal rearrangement induced by bleomycin (BLM) in human fibroblast cells, a 60 Hz ELF-EMF of 0.8 mT field strength was applied either alone or with ELM throughout the culture period and a micronucleus-centromere assay was performed. Our results indicate that the frequencies of MN, aneuploidy and chromosomal rearrangement induced by ELM increased in a dose-dependent manner. The exposure of cells to 0.8 mT ELF-EMF followed by ELM exposure for 3 hours led to significant increases in the frequencies of MN and aneuploidy compared to BLM treatment for 3 hours alone (p<0.05), but no significant difference was observed between field exposed and sham exposed control cells. The obtained results suggest that low density ELF-EMF could act as an enhancer of the initiation process of BLM rather than as an initiator of mutagenic effects in human fibroblast.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.1
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• pp.75-82
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• 2000

In this paper, a new low power 16-bit ALU has been designed, fabricated and tested at the transistor level. The designed ALU performs 16 instructions and has a two-stage pipelined architecture. For the reduction of switched capacitance, the ELM adder of the proposed ALU is inactive while the logical operation is performed and P(propagation) block has a dual bus architecture. A new efficient P and G(generation) blocks are also proposed for the above ALU architecture. ELM adder, double-edge triggered register and the combination of logic style are used for low power consumption as well. As a result of simulations, the proposed architecture shows better power efficient than conventional architecture$^{[1,2]}$ as the number of logic operation to be performed is increased over that of arithmetic to logic operation to be performed is 7 to 3, compared to conventional architecture. The proposed ALU was fabricated with 0.6${\mu}m$ single-poly triple-metal CMOS process. As a result of chip test, the maximum operating frequency is 53MHz and power consumption is 33mW at 50MHz, 3.3V.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.1
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• pp.56-64
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• 2011

Ecotourism is an alternative tourism which can be defined as responsible tourism in natural areas. This study deals with changing the tourists' environmental attitudes of ecotourism activities based on the elaboration likelihood model(ELM). The surveys were conducted based on the same participants to clearly measure the differences of environmental attitudes between pre and post-participation ecotourism activities. A total of 183 samples were collected in Upo Wetlands. The results showed that there were significant differences in the tourists' environmental attitudes between pre and post-participation ecotourism activities. This indicates that ecotourism participants delicately reinforce their eco-friendly attitudes through various ecotourism experiences and educational programs. The most significant finding is that anti-environmental behavior can be relieved with eco-friendliness through participation in ecotourism.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.114-123
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• 2016

The effective feature extraction method for unmanned aerial vehicle (UAV) detection is proposed and verified in this paper. The UAV engine sound is harmonic complex tone whose frequency ratio is integer and its variation is continuous in time. Using these characteristic, we propose the feature vector composed of a mean and standard deviation of difference value between fundamental frequency with 1st overtone as well as mean variation of their frequency. It was revealed by simulation that the suggested feature vector has excellent discrimination in target signal identification from various interfering signals including frequency variation with time. By comparing Fisher scores, three features based on frequency show outstanding discrimination of measured UAV signals with low signal to noise ratio (SNR). Detection performance with simulated interference signal is compared by MFCC by using ELM classifier and the suggested feature vector shows 37.6% of performance improvement As the SNR increases with time, the proposed feature can detect the target signal ahead of MFCC that needs 4.5 dB higher signal power to detect the target.

• Proceedings of the Korean Fiber Society Conference
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• 2002.04a
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• pp.462-465
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• 2002

물에 용해되어 있는 금속이온의 분리를 위한 선택성이 있는 분리 막의 개발은 환경오염이 날로 심각해지고 있는 오늘날에 환경오염의 개선, 에너지절약, 자원의 재활용 등 문제를 해결함에 있어서 아주 중요한 작용을 하게 될 것이다. 물에 용해되어 있는 금속이온을 선택적으로 분리하기 위해 일반적으로 캐리어(carrier)를 함유한 Carrier-Facilitated Transport Membrane (CFM)을 이용하게 되는데, 이 방면에 대한 연구는 주로 유기 상에 용해되어 있는 캐리어를 microporous한 필름에 지지하게 하는 방식으로 만들어진 Supported Liquid Membrane (SLM) 혹은 Elusion Liquid Membrance (ELM)의 개발에 대한 팽대한 연구가 이루어 졌다(1). (중략)