• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.176-185
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• 2018

With the recent development of manufacturing technology and the diversification of consumer needs, not only the process and quality control of production have become more complicated but also the kinds of information that manufacturing facilities provide the user about process have been diversified. Therefore the importance of big data analysis also has been raised. However, most small and medium enterprises (SMEs) lack the systematic infrastructure of big data management and analysis. In particular, due to the nature of domestic manufacturing companies that rely on foreign manufacturers for most of their manufacturing facilities, the need for their own data analysis and manufacturing support applications is increasing and research has been conducted in Korea. This study proposes integrated analysis platform for process and quality analysis, considering manufacturing big data database (DB) and data characteristics. The platform is implemented in two versions, Web and C/S, to enhance accessibility which perform template based quality analysis and real-time monitoring. The user can upload data from their local PC or DB and run analysis by combining single analysis module in template in a way they want since the platform is not optimized for a particular manufacturing process. Also Java and R are used as the development language for ease of system supplementation. It is expected that the platform will be available at a low price and evolve the ability of quality analysis in SMEs.

• Membrane and Water Treatment
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• v.6 no.1
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• pp.77-94
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• 2015

Ultrafiltration membranes have several advantages over conventional drinking-water treatment. However, this technology presents major limitations, such as irreversible fouling and low removal of natural organic matter. Fouling depends heavily on the raw-water quality as well as on the operating conditions of the process, including flux, permeate recovery, pre-treatment, chemical cleaning, and backwashing. Starting with the premise that the optimisation of operating variables can improve membrane performance, different experiments were conducted in a pilot plant located in Granada (Spain). Several combinations of permeate and backwashing flow rates, backwashing frequencies, and aeration flow rates were tested for low-quality water coming from Genil River with the following results: the effluent quality did not depend on the combination of operating conditions chosen; and the membrane was effective for the removal of microorganisms, turbidity and suspended solids but the yields for the removal of dissolved organic carbon were extremely low. In addition, the threshold transmembrane pressure (-0.7 bar) was reached within a few hours and it was difficult to recover due to the low efficiency of the chemical cleanings. Moreover, greater transmembrane pressure due to fouling also increased the energy consumption, and it was not possible to lower it without compromising the permeate recovery. Finally, the intensification of aeration contributed positively to lengthening the operation times but again raised energy consumption. In light of these findings, the feasibility of ultrafiltration as a single treatment is questioned for low-quality influents.

• Journal of Microbiology and Biotechnology
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• v.10 no.5
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• pp.587-594
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• 2000

Monoclonal antibodies against glutamate dehydrogenase (GDH) from Sulfolobus solfataricus were produced and characterized using epitope mapping and biosensor technology, Five monoclonal antibodies raised against S. solfataricus GDH were each identified as a single protein band that comigrated with purified S. solfataricus GDH on the SDS-polyacrylamide gel electrophoresis and immunoblot. Epitope mapping analysis showed that only one subgroup among the antibodies tested recognized the same peptide fragments of GDH. Using the anti-S. solfataricus GDH antibodies as probes, the cross-reactivities of GDHs from various sources were investigated and it was found that the mammalian GDH is not immunologically related to S. solfataricus GDH. The structural differences between the microbial and mammalian GDHs were further investigated using biosensor technology (Pharmacia BIAcore) and monoclonal antibodies against S. solfataricus and bovine brain. The binding affinity of S. solfataricus glutamate dehydrogenase anti-S. solfataricus for GDH ($K_D$=11 nM) was much tighter than that of anti-bovine for GDH ($K_D$=450 nM). These results, together with the epitope mapping analysis, suggest that there may be structural differences between the two GDH species, in addition to their different biochemical properties.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.1
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• pp.81-87
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• 2020

Recently, the network configuration is being rapidly changed to enable easy and free network service configuration based on SDN/NFV. Despite the many advantages and applications of SDN, many security issues such as Distributed Denial of Service (DDoS) attacks are being constantly raised as research issues. In particular, the effectiveness of DDoS attacks is much faster, SDN is causing more and more fatal damage. In this paper, we propose an entropy-based technique to detect and mitigate DDoS attacks in SDN, and prove it through experiments. The proposed scheme is designed to mitigate these attacks by detecting DDoS attacks on single and multiple victim systems and using time - specific techniques. We confirmed the effectiveness of the proposed scheme to reduce packet loss rate by 20(19.86)% while generating 3.21% network congestion.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.397-406
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• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.87-87
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• 2019

The consumption of herbal medicine and related products (herbal products) have increased in South Korea. At the same time the quality, safety, and efficacy of herbal products is being raised. Currently, the herbal products are standardized and controlled according to the requirements of the Korean Pharmacopoeia, the National Institute of Health and the Ministry of Public Health and Social Affairs. The validation of herbal products and their medicinal component is important, since many of these herbal products are composed of two or more medicinal plants. However, there are no tools to support the validation process. Interest in deep learning has exploded over the past decade, for herbal medicine using algorithms to achieve herb recognition, symptom related target prediction, and drug repositioning have been reported. In this study, individual images of four herbs (Panax ginseng C.A. Meyer, Atractylodes macrocephala Koidz, Poria cocos Wolf, Glycyrrhiza uralensis Fischer), actually sold in the market, were achieved. Certain image preprocessing steps such as noise reduction and resize were formatted. After the features are optimized, we applied GoogLeNet_Inception v4 model for herb image recognition. Experimental results show that our method achieved test accuracy of 95%. However, there are two limitations in the current study. Firstly, due to the relatively small data collection (100 images), the training loss is much lower than validation loss which possess overfitting problem. Secondly, herbal products are mostly in a mixture, the applied method cannot be reliable to detect a single herb from a mixture. Thus, further large data collection and improved object detection is needed for better classification.

• Archives of Plastic Surgery
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• v.48 no.1
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• pp.107-113
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• 2021

Background Applicants to integrated plastic and reconstructive surgery (PRS) residency in the United States spend exorbitant amounts of time and money throughout the interview process. Outside of first-hand experience through a visiting rotation, applicants utilize various resources in learning about a program. Today's applicants are "Millennials," the demographic cohort raised during the information age and proficient with digital technology. The authors evaluated whether programs have a presence on social media, and whether applicants are following these accounts. Methods An online survey was sent to applicants to a single integrated plastic surgery program evaluating basic demographics, social media utilization, and sources of information accessed throughout the residency application process. A manual search of popular social media platforms (Instagram, Facebook, and Twitter) was performed in October 2019. Accounts affiliated with integrated PRS programs were identified and analyzed. Results Eighty-four of 222 applicants (37.8%) completed the survey. Ninety-six percent of applicants were within the Millennial demographic. Ninety-six percent of applicants had some form of social media presence, with Facebook (90%) and Instagram (87%) being the most popular platforms. Seventy-three percent of applicants reported following a PRS residency social media account. As of October 2019, 59 integrated residency programs (73%) have active Instagram accounts. Conclusions Applicants still rely on the program website when researching potential residencies, but social media is being rapidly adopted by programs. Program social media accounts should be used as a dynamic form of communication to better inform applicants of program strengths and weaknesses.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.421-437
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• 2020

Problem/Project based learning(PBL) is a student-centered teaching method in which students collaboratively solve problems and reflect their experiences. According to the results of PBL study and the experiences of the authors in PBL instruction, this paper introduced the necessities, output and significance of learning process PBL evaluation method and sums up our PBL evaluation process. The issue of appropriate and fair evaluation has been raised in untact (non-contact) university mathematics education due to the novel coronavirus (COVID-19) of the year 2020. To this end, when we had the course on for the summer semester held at S University in the summer of 2020. To ensure the fairness in evaluation and to improve the quality of our college math education, the PBL evaluation method was fully adapted. As a result, most of the students who took the lecture have learned a wide range of related knowledge without a single exception, and students agreed it is an ideal, fair, rational, and effective evaluation method applicable to other online courses in the era of untact education. This case was summarized in detail and introduced in this paper.

• Computers and Concrete
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• v.32 no.1
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• pp.75-85
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• 2023

This work utilizes simplified higher-order shear deformation beam theory (HSDBT) to investigate the vibration response for functionally graded carbon nanotube-reinforced composite (CNTRC) beam. Novel to this work, single-walled carbon nanotubes (SWCNTs) are distributed and aligned in a matrix of polymer throughout the beam, resting on a viscoelastic foundation. Four un-similar patterns of reinforcement distribution functions are investigated for the CNTRC beam. Porosity is another consideration taken into account due to its significant effect on functionally graded materials (FGMs) properties. Three types of uneven porosity distributions are studied in this study. The damping coefficient and Winkler's and Pasternak's parameters are considered in investigating the viscosity effect on the foundation. Moreover, the impact of different parameters on the vibration of the CNTRC beam supported by a viscoelastic foundation is discussed. A comparison to other works is made to validate numerical results in addition to analytical discussions. The findings indicate that incorporating a damping coefficient can improve the vibration performance, especially when the spring constant factors are raised. Additionally, it has been noted that the fundamental frequency of a beam increases as the porosity coefficient increases, indicating that porosity may have a significant impact on the vibrational characteristics of beams.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.