• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1076-1087
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• 2010

Together with a new advent of embedded processor developed to support specific application area and it evolution, a new research of software development to support the embedded processor and its commercial challenge has been revitalized. Retargetability is typically achieved by providing target machine information, ADL, as input. The ADLs are used to specify processor and memory architectures and generate software toolkit including compiler, simulator, assembler, profiler, and debugger. The EXPRESSION ADL follows a mixed level approach-it can capture both the structure and behavior supporting a natural specification of the programmable architectures consisting of processor cores, coprocessors, and memories. And it was originally designed to capture processor/memory architectures and generate software toolkit to enable compiler-in-the-loop exploration of SoC architecture. In this paper, we designed the class-based ADL based on the EXPRESSION ADL to promote the write-ability, extensibility and verified the validation of grammar. For this works, we defined 6 core classes and generated the EXPRESSION's compiler and simulator through the MIPS R4000 description.

• Korean Journal of Veterinary Service
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• v.40 no.4
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• pp.253-258
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• 2017

The aim of this study is to develop an optimal analytical method for gonadorelin, progesterone, oxytocin and estradiol, the major components of hormones. A relatively simple and reproducible method using high performance liquid chromatography was developed and as a result of the measurement of specificity, linearity, repeatability, accuracy and intermediated precision, the validity of the developed method was verified with the result of meeting the verification criteria of analytical method validation. Using this newly developed method, 12 post-market veterinary products were tested and the ingredient content were 91.9~116.4%, which satisfied the 90~120% condition of the administrative measure standard. Therefore, if the newly developed method is used for the collection examination of hormone in veterinary medicine, it can be useful as an approved test method.

• Journal of Ecology and Environment
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• v.35 no.2
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• pp.91-98
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• 2012

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as $Pinus$ $densiflora-P.$ $rigida$ and $Quercus$ $mongolica-Q.$ $serrata$, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of $Pterocarya$ $stenoptera$, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. $P.$ $densiflora-P.$ $rigida$ and $Q.$ $mongolica-Q.$ $serrata$ were classified as the same species because they did not show significant differences in terms of spectral patterns.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.89-102
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• 2018

In this paper, the security characteristics of healthcare institutions were derived through analysis of previous research, and the characteristics and status of small and medium sized healthcare institutions were surveyed through field surveys of small and medium sized healthcare institutions. The security management evaluation model for small and medium sized healthcare institutions was designed and verified based on the security characteristics of small and medium healthcare institutions. For the design, we compared and analyzed existing security management system and evaluation certification system of healthcare institutions. We also confirmed the proposed security management evaluation model and the degree of sharing. In addition, we conducted validation for the statistical verification of the proposed security management evaluation model for small and medium sized healthcare institutions, and we performed the relative priority analysis through AHP analysis to derive the weight for each item. The result of this study is expected to be used as a standard of security management evaluation model that can be practiced in small and medium sized healthcare institutions.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.589-598
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• 2018

To maintain safety and reliability of reactors, redundant sensors are usually used to measure critical variables and estimate their averaged time-dependency. Nonhealthy sensors can badly influence the estimation result of the process variable. Since online condition monitoring was introduced, the online cross-calibration method has been widely used to detect any anomaly of sensor readings among the redundant group. The cross-calibration method has four main averaging techniques: simple averaging, band averaging, weighted averaging, and parity space averaging (PSA). PSA is used to weigh redundant signals based on their error bounds and their band consistency. Using the consistency weighting factor (C), PSA assigns more weight to consistent signals that have shared bands, based on how many bands they share, and gives inconsistent signals of very low weight. In this article, three approaches are introduced for improving the PSA technique: the first is to add another consistency factor, so called trend consistency (TC), to include a consideration of the preserving of any characteristic edge that reflects the behavior of equipment/component measured by the process parameter; the second approach proposes replacing the error bound/accuracy based weighting factor ($W^a$) with a weighting factor based on the Euclidean distance ($W^d$), and the third approach proposes applying $W^d$, TC, and C, all together. Cold neutron source data sets of four redundant hydrogen pressure transmitters from a research reactor were used to perform the validation and verification. Results showed that the second and third modified approaches lead to reasonable improvement of the PSA technique. All approaches implemented in this study were similar in that they have the capability to (1) identify and isolate a drifted sensor that should undergo calibration, (2) identify a faulty sensor/s due to long and continuous missing data range, and (3) identify a healthy sensor.

• Food Science and Biotechnology
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• v.14 no.1
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• pp.42-45
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• 2005

The present study was carried out to envisage the aerobic growth of Listeria monocytogenes and Yersinia enterocolitica on pork, which is one of the major meat sources in Korea. The results were compared with the previously developed predictive model systems for the verification of microbial growth in a real situation during pork processing. Pork loin samples (8.0 g, 5 mm thick) were aseptically prepared and inoculated with each pathogen by immersing into the respective inoculums for one min. Each of the samples were then wrapped with PE film and stored at 5, 10, and $15^{\circ}C$ up to 36 days to measure the growth profile of the respective pathogens. The growth parameters were calculated by using Gompertz equation and were compared with the previously reported data. The predicted generation time (GT) of L. monocytogenes at 5, 10 and $15^{\circ}C$ was 28.74, 7.85 and 4.02 hr, respectively, and for Y. enterocolitica was 10.29, 4.74 and 2.50 hr, at the same temperatures respectively. In this study, the GT values predicted on pork were slightly higher than the values predicted in other studies using liquid model systems. Unlike previous reports, both the pathogens were found to grow at $5^{\circ}C$ on pork. This finding recommends the necessity of controlling the growth of both the pathogens during the slaughtering process and distribution.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1167-1187
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• 2020

We are facing the situation where cyber threats such as hacking, malware, data leakage, and theft, become an important issue in the perspective of personal daily life, business, and national security. Although various efforts are being made to response to the cyber threats in the national and industrial sectors, the problems such as the industry-academia skill-gap, shortage of cybersecurity professionals are still serious. Thus, in order to overcome the skill-gap and shortage problems, we propose a Cybersecurity technical response Job Competency(CtrJC) framework by adopting the concept of cybersecurity personnel's job competency. As a sample use-case study, we implement the CtrJC against to personals who are charged in realtime cybersecurity response, which is an important job at the national and organization level, and verify the our framework's effects. We implement a sample model, which is a CtrJC against to realtime cyber threats (We call it as CtrJC-R), and study the verification and validation of the implemented model.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.891-897
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• 2021

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.

• Nuclear Engineering and Technology
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• v.27 no.4
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• pp.571-581
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• 1995

The generation, verification and validation of Emergency Procedures for Nuclear Power Plant is a difficult and complex process. Atomic Energy Control Board(AECB) requires that emergency procedure and plan be produced before obtaining the Operating License, that is, detailed plans and procedures to handle emergency situations for both on-site actions and off-site actions be developed. In this report Emergency Operating Procedures Standard for Canadian Nuclear Utilities which makes reference to U. S. practices and the current direction of emergency procedures for CAN-DU reactors are reviewed and compared based on scope(events covered), methodology (event-oriented or symptom-oriented or hybrid) and format(method of presentation) preponderantly, and an attempt is made to integrate these procedures and as a result the recommended strategy for Wolsong unit 2, 3, & 4 is presented as event-specific procedures, generic procedures(when event is not diagnosed) and whose format is combination of logic diagram and text.