• 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.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.8
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• pp.598-608
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• 2002

This paper shows the optimal design methodology for the fluid engine mount by the artificial life algorithm. The design has been commonly modified by trial and error because there is many design parameters that can be varied in order to minimize transmissibility at the desired fundamental resonant and notch frequencies. The application of trial and error method to optimization of the fluid mount is a great work. Many combinations of parameters are possible to give us the desired resonant and notch frequencies, but the question is which combination Provides the lowest resonant peak and notch depth. In this study the enhanced artificial life algorithm is applied to get the desired fundamental resonant and notch frequencies of a fluid mount and to minimize transmissibility at these frequencies. The present hybrid algorithm is the synthesis of and artificial life algorithm with the random tabu (R-tabu) search method. The hybrid algorithm has some advantages, which is not only faster than the conventional artificial life algorithm, but also gives a more accurate solution. In addition, this algorithm can find all globa1 optimum solutions. The results show that the performance of the optimized mount compared with the original mount is improved significantly.

• Bulletin of the Korean Chemical Society
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• v.25 no.12
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• pp.1917-1923
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• 2004

Although the proteolytic activity of the Cu(II) complex of cyclen (Cyc) is greatly enhanced upon attachment to a cross-linked polystyrene (PS), the Cu(II)Cyc-containing PS derivatives reported previously hydrolyzed only a very limited number of proteins. The PS-based artificial metalloproteases can overcome thermal, mechanical, and chemical instabilities of natural proteases, but the narrow substrate selectivity of the artificial metalloproteases limits their industrial application. In the present study, artificial metalloproteases exhibiting broad substrate selectivity were synthesized by attaching Cu(II)Cyc to a PS derivative using linkers with various structures in an attempt to facilitate the interaction of various protein substrates with the PS surface. The new artificial metalloproteases hydrolyzed all of the four protein substrates (albumin, myoglobin, ${\gamma}$-globulin, and lysozyme) examined, manifesting $k_{cat}/K_m$ values of 28-1500 $h_{-1}M_{-1}$ at 50 $^{\circ}C$. The improvement in substrate selectivity is attributed to steric and/or polar interaction between the bound protein and the PS surface as well as the hydrophobicity of the microenvironment of the catalytic centers.