• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.1911-1920
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• 2005

Effective artificial metalloproteases have been designed by using cross-linked polystyrene as the backbone. Artificial active sites comprising Cu(II) complexes as the catalytic site and other metal centers or organic functionalities as binding sites were synthesized. The activity of Cu(II) centers for peptide hydrolysis was greatly enhanced on attachment to polystyrene. By placing binding sites in proximity to the catalytic centers, the ability to hydrolyze a variety of protein substrates at selected cleavage sites was improved. Thus far, the most advanced immobile artificial proteases have been obtained by attaching the aldehyde group in proximity to the Cu(II) complex of cyclen.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.449-459
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• 2016

This paper proposes a new method to improve the three problems of existing automatic composition method using artificial neural networks. The first problem is that the existing beat post-processing to fit into music theories could not handle all the cases of occurring. The second one is that the pitch space generated by artificial neural networks is distorted because the rest is trained with the pitch on the same neural network with large values. The last problem is caused by the difference between the initial melody and beats given by user and those generated by an artificial neural network in the process of new composition. In order to treat these problems, we propose an enhanced post-processing of beats, initial melody processing, and adoption of artificial neural network for rest. It was found from experiments that the proposed methods totally resolved the three problems.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.349-356
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• 2005

Artificial Organism-used application areas are expanding at a break-neck speed with a view to getting things done in a dynamic and Informal environment. A use of general programming or traditional hi methods as the representation of Artificial Organism behavior knowledge in these areas can cause problems related to frequent modifications and bad response in an unpredictable situation. Strategies aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But the learning method of Artificial-Organism is not good yet, and can't represent life in the environment. With this in mind, this research is designed to come up with a new behavior evolution model. The model represents behavior knowledge with Classification Rules and Enhanced Backpropation Neural Networks and discriminate the denomination. To evaluate the model, the researcher applied it to problems with the competition of Artificial-Organism in the Simulator and compared with other system. The survey shows that the model prevails in terms of the speed and Qualify of learning. The model is characterized by the simultaneous learning of classification rules and neural networks represented on chromosomes with the help of Genetic Algorithm and the consolidation of learning ability caused by the hybrid processing of the classification rules and Enhanced Backpropagation Neural Network.

• Food Science and Biotechnology
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• v.14 no.4
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• pp.547-550
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• 2005

Fourteen out of 87 strains of lactic acid bacteria isolated tram Kimchi were found to be resistant against the action of artificial gastric and bile juices. In particular, lactobacilli KM 3, 7, 28, and 37 showed strong resistance and their viable cell counts at the initial stage remained the same even after 3 hours of cultivation in an artificial gastric juice. However, the survival rates of KM 14, 28, and 64 were found to be significantly enhanced in artificial bile juice. Based on the paper disc method, it was evident that isolated lactic acid bacteria showed antibacterial effect against Listeria monocytogenes, Escherichia coli, Bacillus subtilis, Staphylococcus aureus, Vibrio vulnificus, and Salmonella typhimurium. The isolated lactic acid bacteria were identified as Lactobacillus plantarum and Leuconostoc mesenteroides.

• Transactions of Materials Processing
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• v.19 no.1
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• pp.5-10
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• 2010

The current understanding for phase/grain boundary sliding and low-temperature/high-strain rate superplasticity of two-phase titanium alloys is summarized. The quantitative analysis on boundary sliding revealed increased sliding resistance on the order of ${\alpha}/{\beta}\;\ll\;{\alpha}/{\alpha}\;{\approx}\;{\beta}/{\beta}$ boundary, hence, led to the conclusion that approximately 50% alpha(or beta) volume fraction and/or grain refinement is beneficial for obtaining large superplastic elongation at low temperature and/or high strain rate. To predict the temperature for 50% alpha volume in various alpha/beta Ti, artificial neural network was applied. Finally, much enhanced superplasticity was achieved through grain refinement utilizing dynamic globularization.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.28 no.2
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• pp.415-433
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• 1998

Digora system is an intraoral indirect digital radiography system utilizing storage phosphor image plate. It has wide dynamic range which allows it to decrease the patient s exposure time and may increase diagnostic ability through image processing (such as edge enhancement, grey scale conversion, brightness change, and contrast enhancement). And also, it can transmit and storage image information. The purpose of this study was to evaluate the diagnostic ability of artificial proximal caries between Conventional radiograph and Digora images(unenhanced image, brightness & contrast controlled image, and edge enhanced image). ROC(Receiver Operating Characteristic) analysis, paired t-tests, and F-tests were done for the statistical evaluation of detectability. The following results were acquired: 1. In Grade I lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.953, 0.933, 0.965, 0.978 (p>0.05). 2. In Grade II lesions, the mean ROC areas of Conventional radiograph, Digora unenhanced image, Digora controlled image, and Digora edge enhanced image were 0.969, 0.964, 0.988, 0.994. Among theses areas, there was just statistical significance between Diagnostic abilities of Digora edge enhanced image and Conventional radiograph (p<0.05). 3. In the Interobserver variability, the ROC curve areas of Digora edge enhanced image was lowermost in these areas, regardless of the Carious lesion depths. In conclusion, intraoral indirect digital system, Digora system, has the potential possibility as an alternative of Conventional radiograph in the diagnosis of proximal caries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2688-2703
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• 2022

Research on the advanced detection of harmful objects in airport cargo for passenger safety against terrorism has increased recently. However, because associated studies are primarily focused on the detection of relatively large objects, research on the detection of small objects is lacking, and the detection performance for small objects has remained considerably low. Here, we verified the limitations of existing research on object detection and developed a new model called the Small Hazardous Object detection enhanced and reconstructed Model based on the You Only Look Once version 5 (YOLOv5) algorithm to overcome these limitations. We also examined the performance of the proposed model through different experiments based on YOLOv5, a recently launched object detection model. The detection performance of our model was found to be enhanced by 0.3 in terms of the mean average precision (mAP) index and 1.1 in terms of mAP (.5:.95) with respect to the YOLOv5 model. The proposed model is especially useful for the detection of small objects of different types in overlapping environments where objects of different sizes are densely packed. The contributions of the study are reconstructed layers for the Small Hazardous Object detection enhanced and reconstructed Model based on YOLOv5 and the non-requirement of data preprocessing for immediate industrial application without any performance degradation.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• Korean Journal of Artificial Intelligence
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• v.12 no.3
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• pp.33-39
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• 2024

In this paper, we explore the application of Kodaly hand signs in enhancing children's music education, performances, and auditory assistance technologies. This research focuses on improving the recognition rate of Multilayer Perceptron (MLP) models in identifying Kodaly hand sign scales through the integration of Artificial Neural Networks (ANN). We developed an enhanced MLP model by augmenting it with additional parameters and optimizing the number of hidden layers, aiming to substantially increase the model's accuracy and efficiency. The augmented model demonstrated a significant improvement in recognizing complex hand sign sequences, achieving a higher accuracy compared to previous methods. These advancements suggest that our approach can greatly benefit music education and the development of auditory assistance technologies by providing more reliable and precise recognition of Kodaly hand signs. This study confirms the potential of parameter augmentation and hidden layers optimization in refining the capabilities of neural network models for practical applications.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.290-293
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• 2006

From the hydraulic experiment, it was concluded that upwelling could be enhanced when the relative structure height (the ratio of structure height to water depth) was 0.3 and stratification parameter was 3.0. In addition, the optimum size of rubbers was determined that the effect of the mean horizontal length of block was affected incident velocity than size of block. In the numerical experiment, the relation between the shape of rubber and stratification parameter was verified, ana the hydraulic characteristics of 3-D flow field around the artificial structures were investigated. Phenomena of flow field around the artificial upwelling structures corresponded with the results of hydraulic experiment. The position with maximum velocity in artificial upwelling structure was the center of top of its front side and the slip stream occurred at the inside and behind-bottom of artificial upwelling structures. The velocity of slip stream and early amplitude of velocity were higher in the inside than the behind-bottom.