• Composites Research
• /
• v.31 no.5
• /
• pp.246-250
• /
• 2018

In this paper, laser ultrasonic rotation scanning method was proposed to inspect and visualize defects in corner section of curved composite structure. L-shaped composite specimen with defects in its corner section were inspected using laser ultrasonic rotation scanning method. L-shaped specimens had artificial defects at three different depths to simulate delamination damage. All artificial defects were detected clearly in different time-of-flight according to their depths. Inspection result showed that the proposed method is suitable to inspect round corner section of curved composite structure without any special tools.

• Structural Engineering and Mechanics
• /
• v.54 no.2
• /
• pp.305-317
• /
• 2015

Optimal sensor placement (OSP) is an integral component in the design of an effective structural health monitoring (SHM) system. This paper describes the implementation of a novel collaborative-climb monkey algorithm (CMA), which combines the artificial fish swarm algorithm (AFSA) with the monkey algorithm (MA), as a strategy for the optimal placement of a predefined number of sensors. Different from the original MA, the dual-structure coding method is adopted for the representation of design variables. The collaborative-climb process that can make the full use of the monkeys' experiences to guide the movement is proposed and incorporated in the CMA to speed up the search efficiency of the algorithm. The effectiveness of the proposed algorithm is demonstrated by a numerical example with a high-rise structure. The results show that the proposed CMA algorithm can provide a robust design for sensor networks, which exhibits superior convergence characteristics when compared to the original MA using the dual-structure coding method.

• Journal of the Korean Geotechnical Society
• /
• v.21 no.9
• /
• pp.25-34
• /
• 2005

This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.

• Ecology and Resilient Infrastructure
• /
• v.2 no.1
• /
• pp.54-63
• /
• 2015

This study was conducted to investigate the flora and vegetation structure at a 15-year-old artificial wetland for the water purification in Jincheon, Korea. The percentage of species number of obligate wetland plants and facultative wetland plants totaled 40%, whereas that of obligate upland plants and facultative upland plants was 57%. This result showed that the artificial wetland in the study experienced terrestrialization. The number of annual and biennial plants that are pioneer vegetation in a successional stage was lower than that of perennial herbs as a result of the long-term stabilization of vegetation. From the results of DCA (detrended correspondence analysis), water depth played an important role on the classification of vegetation structure in an old artificial wetland. Species diversity was higher in the terrestrialized plant communities such as Iris pseudacorus and Aster koraiensis than in any other wetland communities. Plant communities could be classified according to the wetland indices; obligate upland for A. koraiensis community, facultative wetlands for Carex dispalata var. dispalata and I. pseudacorus community, and obligate wetlands for Nymphoides peltata, Nymphaea tetragona, Phragmites communis, Potamogeton maackianus, and Typha angustifolia community. In conclusion, this result suggests that wetland vegetation should be maintained against terrestrialization through the proper management of sedimentation and hydrological regime in an artificial wetland.

• Journal of Broadcast Engineering
• /
• v.27 no.2
• /
• pp.185-197
• /
• 2022

Recently, deep learning networks with high performance for object recognition are emerging. In the case of object recognition using deep learning, it is important to build a training data set to improve performance. To build a data set, we need to collect and label the images. This process requires a lot of time and manpower. For this reason, open data sets are used. However, there are objects that do not have large open data sets. One of them is data required for license plate detection and recognition. Therefore, in this paper, we propose an artificial license plate generator system that can create large data sets by minimizing images. In addition, the detection rate according to the artificial license plate arrangement structure was analyzed. As a result of the analysis, the best layout structure was FVC_III and B, and the most suitable network was D2Det. Although the artificial data set performance was 2-3% lower than that of the actual data set, the time to build the artificial data was about 11 times faster than the time to build the actual data set, proving that it is a time-efficient data set building system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.631-653
• /
• 2020

Sign language is a natural, visually oriented and non-verbal communication channel between people that facilitates communication through facial/bodily expressions, postures and a set of gestures. It is basically used for communication with people who are deaf or hard of hearing. In order to understand such communication quickly and accurately, the design of a successful sign language translation system is considered in this paper. The proposed system includes object detection and classification stages. Firstly, Single Shot Multi Box Detection (SSD) architecture is utilized for hand detection, then a deep learning structure based on the Inception v3 plus Support Vector Machine (SVM) that combines feature extraction and classification stages is proposed to constructively translate the detected hand gestures. A sign language fingerspelling dataset is used for the design of the proposed model. The obtained results and comparative analysis demonstrate the efficiency of using the proposed hybrid structure in sign language translation.

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

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.14 no.3
• /
• pp.244-252
• /
• 2004

The theoretical method is developed to investigate the vibration characteristics for the partially fluid-filled continuous cylindrical shells with the intermediate supports. The intermediate supports are simulated by two types of artificial springs : the translational spring for the translation for each direction and the rotational spring for a rotation. The springs are continuously distributed along the circumferential direction. By allowing the spring stiffness to become very high compared to the stiffness of the structure, the rigid intermediate supports are approximated. In the theoretical procedure, the Love's thin shell theory is adopted to formulate the theoretical model. The frequency equation of the continuous cylindrical shell is derived by the Rayleigh-Ritz approach based on the energy method. Comparison and convergence studies are carried out to verify and establish the appropriate number of series term and the artificial spring stiffness to produce results with an acceptable order of accuracy. The effect of intermediate supports, their positions and fluid level on the natural frequencies and mode shapes are studied.

• Steel and Composite Structures
• /
• v.14 no.4
• /
• pp.367-377
• /
• 2013

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

• Bulletin of the Korean Chemical Society
• /
• v.28 no.9
• /
• pp.1472-1476
• /
• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.