• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Design & Manufacturing
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• v.17 no.4
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• pp.1-7
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• 2023

In the fabrication of curved multi-display glass for automotive use, the surface roughness of the mold is a critical quality factor. However, the difficulty in detecting micro-cutting signals in a micro-machining environment and the absence of a standardized model for predicting micro-cutting forces make it challenging to intuitively infer the correlation between cutting variables and actual surface roughness under machining conditions. Consequently, current practices heavily rely on machining condition optimization through the utilization of cutting models and experimental research for force prediction. To overcome these limitations, this study employs a surface roughness prediction formula instead of a cutting force prediction model and converts the surface roughness prediction formula into experimental data. Additionally, to account for changes in surface roughness during machining runtime, the theory of position variables has been introduced. By leveraging artificial neural network technology, the accuracy of the surface roughness prediction formula model has improved by 98%. Through the application of artificial neural network technology, the surface roughness prediction formula model, with enhanced accuracy, is anticipated to reliably perform the derivation of optimal machining conditions and the prediction of surface roughness in various machining environments at the analytical stage.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.366-366
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• 2016

Nanoscale observation of attachment systems of animals has revealed various exquisite multiscale architectures for essential functions such as gecko's locomotion, beetles' wing fixation, octopuses' sucking and crawling. In particular, the hierarchical 3-dimensional hexanonal nano-architectures in the tree frog's adhesion is known to have the capability of the enhancement of adhesion forces on the wet or rough surfaces due to the conformal contacts against rough surfaces and water-drainable micro channels. Here, we report that tree frog-inspired patches using unique artificial 3-dimensional hexagonal structures can be exploited to form reversibly enhanced adhesion against various highly curved and rough surfaces in dry and wet condition. To investigate the adhesion effect of micro-channels, we changed the arrangement of microstructure and spacing gaps between micro-channels. In addition, we introduced the 3-dimensional hexagonal hierarchical architectures to artificial patches to enhance to conformal contacts on the various rough surfaces such as skin and organs. Using the robust adhesion properties, we demonstrated the self-drainable and comfortable skin-attachable devices which can measure EKG (electrokardiogramme) for in-vitro diagnostics. As a result, bio-inspired programmable nano-architectures can be applied in versatile devices such as, medical patches, skin-attachable electronics etc., which would shed light on future smart, directional and reversible adhesion systems.

• The KSFM Journal of Fluid Machinery
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• v.5 no.4 s.17
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• pp.47-53
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• 2002

The steam generator feedwater flow-rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow-rate in pressurized water reactors, may result in unnecessary plant power derating. The back-propagation network was used to generate models of signals for a pressurized water reactor Multiple-input, single-output hetero-associative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow-rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.113-120
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• 2003

Rayleigh was an excellent experimenter as well as a theorist. Rayleigh improved Rijke's sounding device by heat and the singing flame into sources of pure tones. Above all, his making of the artificial bird whistle was a critical achievement in the improvement of experimental sound sources. This source made supersonic waves available in the laboratory and thus paved the way to confirmable observations of reflection, refraction, diffraction and interference of sound in the laboratory Furthermore, Rayleigh augmented the sensitivity of sensitive flames as detectors for sound wave. Besides, he devised a phonic wheel which could precisely control the angular velocity of some acoustical instruments and made the Rayleigh-disk that enabled experimenters to measure the absolute value of the sound intensity. These devices enhanced the exactness of acoustical experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control school is based on clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• BMB Reports
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• v.40 no.1
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• pp.53-57
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• 2007

The enzymatic properties of NADH:quinone oxidoreductase were examined in Triton X-100 extracts of Bacillus cereus membranes by using the artificial electron acceptors ubiquinone-1 and menadione. Membranes were prepared from B. cereus KCTC 3674 grown aerobically on a complex medium and oxidized with NADH exclusively, whereas deamino-NADH was determined to be poorly oxidized. The NADH oxidase activity was lost completely by solubilization of the membranes with Triton X-100. However, by using the artificial electron acceptors ubiquinone-1 and menadione, NADH oxidation could be observed. The activities of NADH:ubiquinone-1 and NADH:menadione oxidoreductase were enhanced approximately 8-fold and 4-fold, respectively, from the Triton X-100 extracted membranes. The maximum activity of FAD-dependent NADH:ubiquinone-1 oxidoreductase was obtained at about pH 6.0 in the presence of 0.1M NaCl, while the maximum activity of FAD-dependent NADH:menadione oxidoreductase was obtained at about pH 8.0 in the presence of 0.1M NaCl. The activities of the NADH:ubiquinone-1 and NADH:menadione oxidoreductase were very resistant to such respiratory chain inhibitors as rotenone, capsaicin, and $AgNO_3$, whereas these activities were sensitive to 2-heptyl-4-hydroxyquinoline-N-oxide (HQNO). Based on these results, we suggest that the aerobic respiratory chain-linked NADH oxidase system of B. cereus KCTC 3674 possesses an HQNO-sensitive NADH:quinone oxidoreductase that lacks an energy coupling site containing FAD as a cofactor.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.251.2-251.2
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• 2013

Natural photosynthesis utilizes two proteins, photosystem I and photosystem II, to efficiently oxidize water and reduce NADP+ to NADPH. Artificial photosynthesis which mimics this process achieve water splitting through a two-step Z-schematic water splitting process using man-made synthetic materials for hydrogen fuel production. In this study, Z-scheme system was achieved from the hybrid materials which composed of hydrogen production part as photosystem I protein and water oxidizing part as semiconductor BiVO4. Utilizing photosystem I as the hydrogen evolving part overcomes the problems of existing hydrogen evolving p-type semiconductors such as water instability, expensive cost, few available choices and poor red light (>600 nm) absorbance. Some problems of photosystem II, oxygen evolving part of natural photosynthesis, such as demanding isolation process and D1 photo-damage can also be solved by utilizing BiVO4 as the oxygen evolving part. Preceding research has not suggested any protein-inorganic-hybrid Z-scheme composed of both materials from natural photosynthesis and artificial photosynthesis. In this study, to realize this Z-schematic electron transfer, diffusion step of electron carrier, which usually degrades natural photosynthesis efficiency, was eliminated. Instead, BiVO4 and Pt-photosystem I were all linked together by the mediator gold. Synthesized all-solid-state hybrid materials show enhanced hydrogen evolution ability directly from water when illuminated with visible light.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.169-176
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• 2020

The authors developed a nodal network model that simulates the flow of moist air and the thermal behavior of a target area. The nodal network model was enhanced using a parameter estimation technique based on the measured temperature, humidity, and schedule data. However, the nodal model is not good enough for predicting humidity of the target space, having 55.6% of CVRMSE. It is because re-evaporation effect could not be modeled due to uncertain factors in the field measurement. Hence, a data-driven model was introduced using an artificial neural network (ANN). It was found that the data-driven model is suitable for predicting the condensation compared to the nodal model satisfying ASHRAE Guideline with 3.36% of CVRMSE for temprature, relative humidity, and surface temperature on average. The model will be embedded in automated devices for real-time predictive control, to minimize the risk of surface condensation at dressroom in an apartment housing.