• Corrosion Science and Technology
• /
• v.18 no.2
• /
• pp.61-71
• /
• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.8
• /
• pp.1154-1162
• /
• 2021

The transcriptional capacities of target genes are strongly influenced by promoters, whereas few studies have focused on the development of robust, high-performance and cross-species promoters for wide application in different bacteria. In this work, four novel promoters (P_k.rtufB, P_k.r1, P_k.r2, and P_k.r3) were predicted from Ketogulonicigenium robustum and their inconsistency in the -10 and -35 region nucleotide sequences indicated they were different promoters. Their activities were evaluated by using green fluorescent protein (gfp) as a reporter in different species of bacteria, including K. vulgare SPU B805, Pseudomonas putida KT2440, Paracoccus denitrificans PD1222, Bacillus licheniformis and Raoultella ornithinolytica, due to their importance in metabolic engineering. Our results showed that the four promoters had different activities, with P_k.r1 showing the strongest activity in almost all of the experimental bacteria. By comparison with the commonly used promoters of E. coli (tufB, lac, lacUV5), K. vulgare (Psdh, Psndh) and P. putida KT2440 (JE111411), the four promoters showed significant differences due to only 12.62% nucleotide similarities, and relatively higher ability in regulating target gene expression. Further validation experiments confirmed their ability in initiating the target minCD cassette because of the shape changes under the promoter regulation. The overexpression of sorbose dehydrogenase and cytochrome c551 by P_k.r1 and P_k.r2 resulted in a 22.75% enhancement of 2-KGA yield, indicating their potential for practical application in metabolic engineering. This study demonstrates an example of applying bioinformatics to find new biological components for gene operation and provides four novel promoters with broad suitability, which enriches the usable range of promoters to realize accurate regulation in different genetic backgrounds.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.837-852
• /
• 2021

Recurrent neural network (RNN) architectures have been used for language modeling (LM) tasks that require learning long-range word or character sequences. However, the RNN architecture is still suffered from unstable gradients on long-range sequences. To address the issue of long-range sequences, an attention mechanism has been used, showing state-of-the-art (SOTA) performance in all LM tasks. A differentiable neural computer (DNC) is a deep learning architecture using an attention mechanism. The DNC architecture is a neural network augmented with a content-addressable external memory. However, in the write operation, some information unrelated to the input word remains in memory. Moreover, DNCs have been found to perform poorly with low numbers of weight parameters. Therefore, we propose a robust memory deallocation method using a limited retention vector. The limited retention vector determines whether the network increases or decreases its usage of information in external memory according to a threshold. We experimentally evaluate the robustness of a DNC implementing the proposed approach according to the size of the controller and external memory on the enwik8 LM task. When we decreased the number of weight parameters by 32.47%, the proposed DNC showed a low bits-per-character (BPC) degradation of 4.30%, demonstrating the effectiveness of our approach in language modeling tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.1078-1099
• /
• 2021

Traditional image steganography hides secret information by embedding, which inevitably leaves modification traces and is easy to be detected by steganography analysis tools. Since coverless steganography can effectively resist steganalysis, it has become a hotspot in information hiding research recently. Most coverless image steganography (CIS) methods are based on mapping rules, which not only exposes the vulnerability to geometric attacks, but also are less secure due to the revelation of mapping rules. To address the above issues, we introduced camouflage images for steganography instead of directly sending stego-image, which further improves the security performance and information hiding ability of steganography scheme. In particular, based on the different sub-features of stego-image and potential camouflage images, we try to find a larger similarity between them so as to achieve the reversible steganography. Specifically, based on the existing CIS mapping algorithm, we first can establish the correlation between stego-image and secret information and then transmit the camouflage images, which are obtained by reversible sub-feature retrieval algorithm. The received camouflage image can be used to reverse retrieve the stego-image in a public image database. Finally, we can use the same mapping rules to restore secret information. Extensive experimental results demonstrate the better robustness and security of the proposed approach in comparison to state-of-art CIS methods, especially in the robustness of geometric attacks.

• Computers and Concrete
• /
• v.27 no.4
• /
• pp.319-332
• /
• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• Journal of Internet Computing and Services
• /
• v.20 no.5
• /
• pp.57-65
• /
• 2019

Creative works such as webtoon and web novel are part of property rights. However, illegal copies of them are distributed on the internet easily, which raises social issues in today's society. In order to tackle these problems, this paper proposes and presents a blockchain based copyright management system that ensures forgery prevention, robust security features, improving trading performance, cost-effective, and enhanced visibility. The system allows a user to register creative works formally just the same as before registration and also to register simple creative ideas just anytime. In the latter case, if an idea or a thought flashes across through somebody's mind, he or she can register it to the system immediately without formal registration process and afterward, can utilize a way to prove its originality through the system. Regarding large size images and video files of creative works, the system reduces data size and storage volume sharply to be processed by network entities by storing original creative works separately and including only the hash result of creative works to the transactions.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.1
• /
• pp.63-73
• /
• 2021

This paper presents a algorithm for automatic target recognition robust to the influence of the flame in order to track the target by EOTS(Electro-Optical Targeting System) equipped on UAV(Unmanned Aerial Vehicle) when there is aerial target or marine target with flame at the same time. The proposed method converts infrared images of targets and flames into a gradient vector field, and applies each gradient magnitude to a polynomial curve fitting technique to extract polynomial coefficients, and learns them in a shallow neural network model to automatically recognize targets and flames. The performance of the proposed technique was confirmed by utilizing the various infrared image database of the target and flame. Using this algorithm, it can be applied to areas where collision avoidance, forest fire detection, automatic detection and recognition of targets in the air and sea during automatic flight of unmanned aircraft.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.3
• /
• pp.264-271
• /
• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.3
• /
• pp.398-404
• /
• 2020

In this paper, we propose a new design method of signal constellation of the spiral quadrature amplitude modulation (QAM) exploiting a modified gradient descent search algorithm and its binary mapping rule. Unlike the conventional method, the new method, which uses and the constellation optimization algorithm and the maximum number of iterations as a parameter for the iterative design, is more robust to phase noise. And the proposed binary mapping rule significantly reduces the average Hamming distance of the spiral constellation. As a result, the proposed spiral QAM constellation has much improved error performance compared to the conventional ones even in a very severe phase noise environment. It is, therefore, considered that the proposed QAM may be a useful modulation format for coherent optical communication systems and orthogonal frequency division multiplexing (OFDM) systems.

• Advances in nano research
• /
• v.10 no.3
• /
• pp.253-261
• /
• 2021

Polydimethylsiloxane (PDMS) is one of the most widely adopted silicon-based organic polymeric elastomers. Elastomeric nanostructures are normally required to accomplish an explicit mechanical role and correspondingly their mechanical properties are crucial to affect device and material performance. Despite its wide application, the mechanical properties of PDMS are yet fully understood. In particular, the time dependent mechanical response of PDMS has not been fully elucidated. Here, utilizing state-of-the-art PeakForce Quantitative Nanomechanical Mapping (PFQNM) together with Force Volume (FV) and Fast Force Volume (FFV), the elastic moduli of PDMS samples were assessed in a time-dependent fashion. Specifically, the acquisition frequency was discretely changed four orders of magnitude from 0.1 Hz up to 2 kHz. Careful calibrations were done. Force data were fitted with a linearized DMT contact mechanics model considering surface adhesion force. Increased Young's modulus was discovered with increasing acquisition frequency. It was measured 878 ± 274 kPa at 0.1 Hz and increased to 4586 ± 758 kPa at 2 kHz. The robust local probing of mechanical measurement as well as unprecedented high-resolution topography imaging open new avenues for quantitative nanomechanical mapping of soft polymers, and can be extended to soft biological systems.