• Proceedings of the Korean Society of Toxicology Conference
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• 2003.10b
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• pp.29-30
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• 2003

The whole genome sequences, mapped for humans and rodents, and technical capability of monitoring whole genome expression in a high throughput fashion enable us to perform the "whole genome profiling". The major characteristics of this profiling from the toxicological point of view are that the overt phenotypes are not the essential factors for the construction of toxicity database/informatics.(omitted)

• Korean Security Journal
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• no.48
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• pp.113-146
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• 2016

Criminal profiling is a effective and efficient measure for enhancing industrial security of casino business. Particularly, developing criminal profiling of deviant behaviors in casino will help security management to become more effective and efficient in practical ways. Unfortunately, however, there is lack of empirical profiling study in this regard. To fill the vacuum of literature on this topic, this study was purported to create offender profiles of different types of deviant behaviors in casino based on various theories and techniques in criminal profiling literature, such as investigative psychology, linkage analysis, and behavioral evidence analysis. To fulfill the purposes, this study collected behavioral evidence from 90 casino security officers in South Korea. Offenders' behavioral evidence was analyzed to develop offender profiles of seven different types of deviant behaviors, and then the profiles were compared with each profiles that security officers focus on to identify offenders during their work hours. Results showed that, first, there were unique profiles of each type of seven different categories of deviant behaviors in terms of offenders' ways of speaking and acting, their appearance and attitudes. In addition, this study found that there were some amount of gaps between actual offenders' profiles and profiles that security officers have in mind. Based on the results, this study provided policy implications in terms of managing casino industrial security, education and training for security officers, and future study on casino security.

• Korean Journal of Microbiology
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• v.54 no.1
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• pp.53-59
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• 2018

The objective of this study was to investigate the plasmid profiling of multi-drug resistant (MDR) Vibrio in influent (inflow) and effluent (discharged) water samples of fish farms in Jeju, South Korea. MDR isolates identified through disc diffusion susceptibility tests, were subjected to plasmid profiling. One hundred fifty Vibrio isolates were obtained from each influent and effluent water sample. All MDR isolates were subjected to plasmid profiling. Greater number of bacteria were enumerated from effluents (61%) comparing to influents (39%). High incidence of neomycin, sulfamethoxazole, amoxicillin and oxytetracycline resistance was observed among the isolates, which was higher in effluent samples. In contrast, Vibrio isolates were more susceptible to florfenicol, chloramphenicol, ciprofloxacin, and nalidixic acid. Among 99 (influent 39 and effluent 60) MDR isolates, a total of 58 (influent 38 and effluent 20) were found to bear plasmids ranging from 1.7 kb to >10 kb and showed 19 different antibiograms according to the size of plasmids. MDR isolates showed six and four distinct plasmid profiles in influent and effluent, respectively. Effluent samples contained more plasmid-carrying MDR Vibrio isolates with more diverse plasmid profiles and antibiograms, suggesting that fish farm tanks may serve as a reservoir of antibiotic resistance genes. The presence of plasmid-carrying MDR Vibrio isolates in fish farm effluent water may contribute to the dissemination of antibiotic resistance genes to the environments, which ultimately poses threat to human health.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• Journal of Ginseng Research
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• v.48 no.2
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• pp.149-162
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• 2024

Ginseng, the roots of Panax species, is an important medicinal herb used as a tonic. As ginsenosides are key bioactive components of ginseng, holistic chemical profiling of them has provided many insights into understanding ginseng. Mass spectrometry has been a major methodology for profiling, which has been applied to realize numerous goals in ginseng research, such as the discrimination of different species, geographical origins, and ages, and the monitoring of processing and biotransformation. This review summarizes the various applications of ginsenoside profiling in ginseng research over the last three decades that have contributed to expanding our understanding of ginseng. However, we also note that most of the studies overlooked a crucial factor that influences the levels of ginsenosides: genetic variation. To highlight the effects of genetic variation on the chemical contents, we present our results of untargeted and targeted ginsenoside profiling of different genotypes cultivated under identical conditions, in addition to data regarding genome-level genetic diversity. Additionally, we analyze the other limitations of previous studies, such as imperfect variable control, deficient metadata, and lack of additional effort to validate causation. We conclude that the values of ginsenoside profiling studies can be enhanced by overcoming such limitations, as well as by integrating with other -omics techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2781-2800
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• 2016

Emerging attacks aim to access proprietary assets and steal data for business or political motives, such as Operation Aurora and Operation Shady RAT. Skilled Intruders would likely remove their traces on targeted hosts, but their network movements, which are continuously recorded by network devices, cannot be easily eliminated by themselves. However, without complete knowledge about both inbound/outbound and internal traffic, it is difficult for security team to unveil hidden traces of intruders. In this paper, we propose an autonomous anomaly detection system based on behavior profiling and relation mining. The single-hop access profiling model employ a novel linear grouping algorithm PSOLGA to create behavior profiles for each individual server application discovered automatically in historical flow analysis. Besides that, the double-hop access relation model utilizes in-memory graph to mine time-sequenced access relations between different server applications. Using the behavior profiles and relation rules, this approach is able to detect possible anomalies and violations in real-time detection. Finally, the experimental results demonstrate that the designed models are promising in terms of accuracy and computational efficiency.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.2
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• pp.69-74
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• 2020

In this study, we proposed a method for surface profiling aspheric lens molds using a precision displacement sensor with a spatial scanning mechanism. The precision displacement sensor is based on the confocal principle using a broadband light source, providing a 10 nm resolution over a 0.3 mm measurable range. The precision of the sensor, depending on surface slope, was evaluated via Allan deviation analysis. We then developed an automatic surface profiling system by measuring the cross-sectional profile of a lens mold. The precision of the sensor at the flat surface was 10 nm at 10 ms averaging time, while 200 ms averaging time was needed for identical precision at the steepest slope at 25 deg. When we compared the measurement result of the lens mold to a commercial surface profiler, we found that the accuracy of the developed system was less than 90 nm (in terms of 3 sigmas of error) between the two results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.39-49
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• 2020

In this paper, we show that the Chinese standard block cipher SM4 is vulnerable to the side channel attacks and present a countermeasure to resist them. We firstly validate that the secret key of SM4 can be recovered by differential power analysis(DPA) and correlation power analysis(CPA) attacks. Therefore we analyze the vulnerable element caused by power attack and propose a first order masking-based countermeasure to defeat DPA and CPA attacks. Although the proposed countermeasure unfortunately is still vulnerable to the profiling power attacks such as deep learning-based multi layer perceptron(MLP), it can sufficiently overcome the non-profiling attacks such as DPA and CPA.

• Bulletin of the Korean Chemical Society
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• v.34 no.5
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• pp.1467-1472
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• 2013

Metabolomics is a field that studies systematic dynamics and secretion of metabolites from cells to understand biological pathways based on metabolite changes. The metabolic profiling of intact human colorectal tissues was performed using high-resolution magic angle spinning (HR-MAS) NMR spectroscopy, which was unnecessary to extract metabolites from tissues. We used two different groups of samples, which were defined as normal and cancer, from 9 patients with colorectal cancer and investigated the samples in NMR experiments with a water suppression pulse sequence. We applied target profiling and multivariative statistical analysis to the analyzed 1D NMR spectra to identify the metabolites and discriminate between normal and cancer tissues. Cancer tissue showed higher levels of arginine, betaine, glutamate, lysine, taurine and lower levels of glutamine, hypoxanthine, isoleucine, lactate, methionine, pyruvate, tyrosine relative to normal tissue. In the OPLS-DA (orthogonal partial least square discriminant analysis), the score plot showed good separation between the normal and cancer groups. These results suggest that metabolic profiling of colorectal cancer could provide new biomarkers.

• Journal of Microbiology and Biotechnology
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• v.16 no.4
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• pp.543-549
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• 2006

DNA microarray was used to study the transcription profiling of Escherichia coli adapting to acetate as a sole carbon source. Bacteria grown in glucose minimal media were used as a reference. The dynamic expression levels of 3,497 genes were monitored at seven time points during this adaptation. Among the central metabolic genes, the glycolytic and glucose phosphotransferase genes were repressed as the bacteria entered stationary phase, whereas the glyoxylate pathway, TCA cycle, and gluconeogenic genes were induced. Distinct induction or repression patterns were recognized among different pathway genes. For example, the repression of glycolytic genes and the induction of gluconeogenic ones started immediately after glucose was depleted. On the other hand, the regulation of the pentose phosphate pathway genes and glyoxylate genes gradually responded to the glucose depletion or was more related to growth in acetate. When the whole genome was considered, many of the CRP, FadR, and Cra regulons were immediately responsive to the glucose depletion, whereas the $\sigma^s$, Lrp, and IHF regulons were gradually responsive to the glucose depletion. The expression profiling also provided differential regulations between isoenzymes; for example, malic enzymes A (sfcA) and B (maeB). The expression profiles of three genes were confirmed with RT-PCR.