• Journal for History of Mathematics
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• v.12 no.1
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• pp.73-81
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• 1999

We survey the researches on self-reproducing structures on cellular automata. Self-reproduction is the foremost characteristic of life, and cellular automata are ideal model for studying artificial life. From the early studies by Von Neumann to late results on computational models using self-reproducing structures and emergence of self-replication are covered. Also possible applications of self-replicating structures are listed.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• Computers and Concrete
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• v.21 no.1
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• pp.55-66
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• 2018

A large main shock may consist of numerous aftershocks with a short period. The aftershocks induced by a large main shock can cause the collapse of a structure that has been already damaged by the preceding main shock. These aftershocks are important factors in structural damages. Furthermore, despite what is often assumed in seismic design codes, earthquakes do not usually occur as a single event, but as a series of strong aftershocks and even fore shocks. For this reason, this study investigates the effect and potential of consecutive earthquakes on the response and behavior of concrete structures. At first, six moment resisting concrete frames with 3, 5, 7, 10, 12 and 15 stories are designed and analyzed under two different records with seismic sequences from real and artificial cases. The damage states of the model frames were then measured by the Park and Ang's damage index. From the results of this investigation, it is observed that the sequences of ground motions can almost double the accumulated damage and increased response of structures. Therefore, it is certainly insufficient to ignore this effect in the design procedure of structures. Also, the use of artificial seismic sequences as design earthquake can lead to non-conservative prediction of behavior and damage of structures under real seismic sequences.

• Smart Structures and Systems
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• v.26 no.1
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• pp.63-75
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• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.

• Journal of Veterinary Clinics
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• v.16 no.1
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• pp.128-137
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• 1999

Plasma progesterone (P$_4$) concentrations were measured for confirming the estrus observation and for the early pregnancy diagnosis in 130 cows of small farmers. Ultrasonographic examinations were performed from day 30 after artificial insemination to establish the characteristic ultrasonographic appearances of gestational structures in each pregnant stages. Of the 130 cows inseminated, 111 cows (85.4%) were an ovulatory estrus, 12 cows (9.2%) were an unovulatory estrus, and 7 cows (5.4%) were the error of estrus detection, respectively. The accuracy for early pregnancy diagnosis in 111 ovulatory estrus cows achieved when the discriminatory concentration at day 21 after artificial insemination was placed at 3.0 ng-/ml in plasma, was 86.7 % for positive diagnosis and 100% for negative diagnosis, respectively. Pregnancy diagnosis by ultrasonography were performed to evaluate gestational structures from day 30 after artificial insemination in 83 cows. Pregnant cows were 72 of 83 cows. The characteristic ultrasonography of gestational structures in each gestational stages was as follows. The embryo proper was observed within anechoic fetal fluid between 28 and 40 days after insemination, and amnion and embryonic heartbeat was also detected in this period. Between days 41 and 50, embryo proper was detected as an discriminated from head and body, and forelimb buds and hindlimb buds were also observed in this period. Between days 51 and 60, an embryo proper was clearly discriminated from head and body, and fetal movement, forelimb buds and hindlimb buds were observed in this period. Between days 61 and 70, fetus was completely developed, and fetal skeleton, organs and cotyledon were observed. After day 71, each organs of fetus were rapidly developed and a fetus was partially observed in screen because fetus was too big and larger, These results indicate that plasma P$_4$ determination at days 0,6 and 21 after artificial insemination can be utilized for confirming the estrus observation and for early pregnancy diagnosis. Also, ultrasonography was reliable method for early pregnancy diagnosis at day 30 after artificial insemination.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.151-156
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• 2001

Concrete has excellent characteristics as building material and functions relatively well; but it has many problems concerning too heavy weight of the structures. Accordingly, it is the assignment for study in the part of building materials to lighten and high strengthen the weight of concrete structures in order to improve those weak Points; and it seems one of the representative solutions to develop the high strength lightweight aggregate concrete. Based on the experimental results presented, the following conclusions are drawn. The concrete with unit weight of 1.96~2.03t/$m^{2}$, compressive strength of 322~431kgf/$cm^{2}$ was gained. So, it appears that the lightweight aggregate concrete will be useful for low unit weight and high strength lightweight aggregate concrete. In the end, to manufacture artificial lightweight aggregate concrete for construction work is necessary to develope artificial aggregate which has improved performances physically.

• Advances in nano research
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• v.15 no.2
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• pp.165-174
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• 2023

The study investigated the effect of geometric structures of nano-patterned surfaces, such as peak sharpness, height, width, aspect ratio, and spacing, on mechano-bactericidal properties. Here, in silico models were developed to explain surface interactions with Escherichia coli. Numerical solutions were performed based on the finite element method and verified by the artificial neural network method. An E. coli cell adhered to the nano surface formed elastic and creep deformation models, and the cells' maximum deformation, maximum stress, and maximum strain were calculated. The results determined that the increase in peak sharpness, aspect ratio, and spacing values increased the maximum deformation, maximum stress, and maximum strain on E. coli cell. In addition, the results showed that FEM and ANN methods were in good agreement with each other. This study proved that the geometrical structures of nano-patterned surfaces have an important role in the mechano-bactericidal effect.

• Steel and Composite Structures
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• v.51 no.1
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• pp.43-51
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• 2024

In the current study, we aim to evaluate both microstructural characteristics and economic benefits of composite structures from supply chain utilizing AI-based method. In this regard, the various aspects of microstructure of composite materials along with the features of supply chain are discussed and quantified. In addition, the final economic aspects of the composite materials and are also presented. Based on available data, a designed artificial neural network is utilized for prediction of both microstructure and economical feature of the composite material. The results indicate that the supply chain could affect the microstructure of final composite materials which in turn make changes in the mechanical properties and durability of composite materials.

• Genomics & Informatics
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• v.15 no.4
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• pp.142-146
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• 2017

More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.