• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.85-97
• /
• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Smart Structures and Systems
• /
• v.22 no.3
• /
• pp.291-302
• /
• 2018

This paper presents a flexibility based method for damage identification from static measurements in beam-type structures. The response of the beam at the Damaged State is decomposed into the response at the Reference State plus the response at an Incremental State, which represents the effect of damage. The damage is localized by detecting slope discontinuities in the deflection of the structure at the Incremental State. A denoising filtering technique is applied to reduce the effect of experimental noise. The extent of the damage is estimated through comparing the experimental flexural stiffness of the damaged cross-sections with the corresponding values provided by analytical models of cracked beams. The paper illustrates the method by showing a numerical example with two cracks and an experimental case study of a simply supported steel beam with one artificially introduced notch type crack at three damage levels. A Digital Image Correlation system was used to accurately measure the deflections of the beam at a dense measurement grid under a set of point loads. The results indicate that the method can successfully detect and quantify a small damage from the experimental data.

• Smart Structures and Systems
• /
• v.12 no.5
• /
• pp.523-545
• /
• 2013

Piezoelectric and dielectric behaviors of a piezoceramic patch adhesively centered on a carbon composite plate are identified using a robust multi-objective optimization procedure. For this purpose, the patch piezoelectric stress coupling and blocked dielectric constants are automatically evaluated for a wide frequency range and for the different identifiable behaviors. Latters' symmetry conditions are coded in the design plans serving for response surface methodology-based sensitivity analysis and meta-modeling. The identified constants result from the measured and computed open-circuit frequencies deviations minimization by a genetic algorithm that uses meta-model estimated frequencies. Present investigations show that the bonded piezoceramic patch has effective three-dimensional (3D) orthotropic piezoelectric and dielectric behaviors. Besides, the sensitivity analysis indicates that four constants, from eight, dominate the 3D orthotropic behavior, and that the analyses can be reduced to the electromechanically coupled modes only; therefore, in this case, and if only the dominated parameters are optimized while the others keep their nominal values, the resulting piezoelectric and dielectric behaviors are found to be transverse-isotropic. These results can help designing piezoceramics smart composites for various applications like noise, vibration, shape, and health control.

• BMB Reports
• /
• v.40 no.4
• /
• pp.494-500
• /
• 2007

The endophytic bruchid pest Callosobruchus maculatus causes severe damage to storage cowpea seeds, leading to economical losses. For this reason the use of $\alpha$-amylase inhibitors to interfere with the pest digestion process has been an interesting alternative to control bruchids. With this aim, $\alpha$-amylase inhibitors from baru seeds (Dipteryx alata) were isolated by affinity chromatographic procedures, causing enhanced inhibition of C. maculatus and Anthonomus grandis $\alpha$-amylases. To attempt further purification, this fraction was applied onto a reversed-phase HPLC column, generating four peaks with remarkable inhibition toward C. maculatus $\alpha$-amylases. SDS-PAGE and MALDI-ToF analysis identified major proteins of approximately 5.0, 11.0, 20.0 and 55 kDa that showed $\alpha$-amylase inhibition. Results of in vivo bioassays using artificial seeds containing 1.0% (w/w) of baru crude extract revealed 40% cowpea weevil larvae mortality. These results provide evidence that several $\alpha$-amylase inhibitors classes, with biotechnological potential, can be isolated from a single plant species.

• Journal of Microbiology and Biotechnology
• /
• v.25 no.8
• /
• pp.1241-1245
• /
• 2015

The aim of the present work was to examine the putative promoter region of the operon ansPAB and to determine the general elements required for the regulation of transcriptional activity. The transcriptional start site of the ansPAB promoter was determined by using highresolution S1-nuclease mapping. Sequence analysis of this region showed -10 and -35 elements, which were consistent with consensus sequences for R. etli promoters that are recognized by the major form of RNA polymerase containing the σ⁷⁰ transcription factor. Mutation studies affecting several regions located upstream of the transcriptional start site confirmed the importance of these elements on transcriptional expression.

• Journal of Microbiology and Biotechnology
• /
• v.27 no.9
• /
• pp.1576-1585
• /
• 2017

Yeasts, filamentous fungi, and bacteria colonize the surface of fermented sausages during the ripening process. The source of this microbiota is their surrounding environment, and is influenced by the maturing conditions and starter cultures. Debaryomyces hansenii was previously isolated from several dry-cured meat products and associated with the lipolytic and proteolytic changes that occur in these products, influencing their taste and flavor. Therefore, this study isolated the yeast microbiota present in the casing from different meat products ("lomo," "chorizo," and "$salchich{\acute{o}}n$") from the Valle de los Pedroches region in southern Spain. D. hansenii was by far the most abundant species in each product, as all 22 selected isolates were identified as D. hansenii by biochemical and/or molecular methods. In contrast, no yeasts were found in the meat batter. These data constitute the first study of the yeasts present in "lomo" sausages and particularly the highly appreciated Valle de los Pedroches "lomo" sausages. Furthermore, the resistance of these isolates to different pHs, temperatures, and saline stress was studied, together with their catabolic characteristics. Based on the results, certain isolates are proposed as valuable candidate starter cultures that could improve both the manufacture and the flavor of such dry-cured meat products, and provide an understanding of new mechanisms involved in stress tolerance. Applied medium-scale industrial tests are currently in progress.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.3112-3112
• /
• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.719-734
• /
• 2020

This paper proposes a new measurement scheme for estimating the processing level according to risk when performing de-identification in the use of personal information by practitioners in the organization in line with the recently revised Data 3 Act. Our proposed methods considered the surrounding circumstances surrounding the data, not just the data, for risk measurement, and divided the data situation into three categories more systematically so that it can be applied in all areas in a general-purpose environment, the data utilization environment, and the data (self) so that it can be calculated quantitatively based on each context risk according to the presented classification. The proposed method is designed to calculate the risk of existing de-identifiable information in a quantitative manner so that personal information controller in general organizations can use it in practice, not just in the qualitative judgment of experts.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.2
• /
• pp.405-416
• /
• 2022

Current AI technology is improving the quality of life by using machine learning based on data. When using machine learning, transmitting distributed data and collecting it in one place goes through a de-identification process because there is a risk of privacy infringement. De-identification data causes information damage and omission, which degrades the performance of the machine learning process and complicates the preprocessing process. Accordingly, Google announced joint learning in 2016, a method of de-identifying data and learning without the process of collecting data into one server. This paper analyzed the effectiveness by comparing the difference between the learning performance of data that went through the de-identification process of K anonymity and differential privacy reproduction data using actual financial data. As a result of the experiment, the accuracy of original data learning was 79% for k=2, 76% for k=5, 52% for k=7, 50% for 𝜖=1, and 82% for 𝜖=0.1, and 86% for Federated learning.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.4
• /
• pp.328-335
• /
• 2017

In this paper, we propose an efficient method to remove noise component contained in high resolution range profile(HRRP) to improve target identification performance. The proposed method can effectively eliminate the noise component using both the statistical characteristics of the noise component and EMD algorithm. Experimental results show that the proposed method can substantially improve the identification capability, removing the noise component effectively.