• Journal of the Korean Society for Library and Information Science
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• v.45 no.4
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• pp.27-52
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• 2011

The value of the library has always been recognized positively. Economic valuation of libraries and their services uses a concrete methodology that enables the quantification of library value and communication of said value among library stakeholders. This paper presents a comprehensive review of literature on economic valuation of the library based on a total of 89 studies conducted over the last quarter-century. Research on library valuation began in the mid-1990's with the formal exploration of the value of public libraries from a theoretical point of view. In the 2000's, various theories and methodologies were reviewed and put into actual measurement studies. The comprehensive review and analysis point to the need for the development of consistent and reliable set of methods, which will facilitate further application of methods and comparison of results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1125-1125
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• 2001

Micro-scale test methods are producing small-sample size where the conventional physical and chemical tests can not be used (high standard deviation, uncertain sampling conditions, low repeatability). Different small-scale test methods were developed recently for determination of physico-chemical, functional, rheological properties of wheat or wheat dough using miniaturized instruments with sophisticated sample preparation/handling and mechanics (RVA, 2 g mixograph, micro-Z-arm mixer, small-scale noodle maker, micro-baking method etc.). The small-scale methodologies can be used as basic research tools or as technology supported measurements and can be also essential in the early selection for quality traits in breeding programs. The milling as a sample preparation step is essential procedure providing good quality flour or semolina samples from small amount of grain (5-10 g) in a reproducible and reliable way. The aim of present study was to use NIR as quality control tool, and to evaluate the recently developed and manufactured micro-scale lab mill (FQC-2000) produced by Inter-Labor Co. Ltd., Hungary. The milling characteristics of the new instrument were compared to other laboratory mills and the effects of milling action on the chemical composition of fractions were analysed. The fractions were tested with both chemical and near infrared spectroscopic methods. The micro-scale milling resulted significantly different yields, particle size distributions and different fractions from compositional point of view. The near infrared spectra were sensitive enough to distinguish the fractions obtained by different milling procedures. Quantitative NIR calibration equations were developed and tested in order to measure the chemical composition of characteristic milling fractions. Special qualification procedure the PQS (Polar Qualification System) method was used for detecting the differences between fractions obtained by macro and micro-milling procedures. The results and the limitations of PQS method in this application will be discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1C
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• pp.19-27
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• 2011

This paper evaluates the applicability of wave-based nondestructive methodologies and a penetration test for compaction quality measurements during road construction. To evaluate the physical and mechanical properties of compacted subgrade soil layers, soil stiffness gauge (SSG), time domain reflectometry (TDR), and miniature electro-mechanical systems (MEMS) accelerometers were used to nondestructively evaluate the soil response during and after compaction and dynamic cone penetrometer (DCP) profiles were used to evaluate the soil shear strength after compaction was completed. At the field site, two types of soils were compacted with four different compaction equipments and energies. Field testing results indicate that soil parameters evaluated by different testing methods, which are SSG, TDR, MEMS accelerometer, and DCP, are highly correlated. In addition, it is shown that the physical and mechanical tests deployed in this study can be used as alternative methods to the conventional compaction quality evaluation methods when assessing the overall quality and the engineering response of compacted lifts.

• Korean Journal of Agricultural Science
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• v.51 no.1
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• pp.63-77
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• 2024

Yield monitoring systems have become integral to precision agriculture, providing insights into the spatial variability of crop yield and playing an important role in modern harvesting technology. This paper aims to review current research trends in yield monitoring systems, specifically designed for non-grain crops, including cabbages, radishes, potatoes, and tomatoes. A systematic literature survey was conducted to evaluate the performance of various monitoring methods for non-grain crop yields. This study also assesses both mass- and volume-based yield monitoring systems to provide precise evaluations of agricultural productivity. Integrating load cell technology enables precise mass flow rate measurements and cumulative weighing, offering an accurate representation of crop yields, and the incorporation of image-based analysis enhances the overall system accuracy by facilitating volumetric flow rate calculations and refined volume estimations. Mass flow methods, including weighing, force impact, and radiometric approaches, have demonstrated impressive results, with some measurement error levels below 5%. Volume flow methods, including paddle wheel and optical methodologies, yielded error levels below 3%. Signal processing and correction measures also play a crucial role in achieving accurate yield estimations. Moreover, the selection of sensing approach, sensor layout, and mounting significantly influence the performance of monitoring systems for specific crops.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.453-467
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• 2022

This study aims to conduct a content analysis of research on workplace counseling with a focus on workplace counselors. This study analyzed a total of 22 research papers on workplace counselors published until January 2021 including thesis, dissertation and journal articles, focusing on year of publication, research topics, subjects, and methodologies. The results are as follows. The number of publications in this area has been increasing continuously since 2008. The most frequently studied topics were roles and competencies of workplace counselors and workplace counselors' adjustment to the job. Workplace counselors(77%) were the most commonly studied research subjects and all three parties of workplace counseling(18%) followed. The qualitative method was employed way more (63%) than mixed methods(27%) and quantitative method(9%). The implications of the findings and recommendations for future research on the basis of the results of this study are provided.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Journal of Advanced Navigation Technology
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• v.19 no.1
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• pp.15-21
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• 2015

WA-DGNSS is a system to service for users using a satellite which received correction data from ground station that calculates the relative errors of the tracked GNSS signals and sends to a satellite. Users are guaranteed the reliability of the GNSS signal and the accuracy of positioning. ICAO recommends the application of WA-DGNSS for the airplane taking off and landing process. In this paper, we suggests methods to verify of the pre-developed WA-DGNSS reference software constituting modules and an integration test process refer to the RTCA DO-278 which is a document for the development process of an aeronautics software. Also, we statistically verified the reference software test through our methods. And then, we confirmed to performance the function of the reference software properly.

• Mass Spectrometry Letters
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• v.15 no.1
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• pp.1-25
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• 2024

Protein glycosylation, a highly significant and ubiquitous post-translational modification (PTM) in eukaryotic cells, has attracted considerable research interest due to its pivotal role in a wide array of essential biological processes. Conducting a comprehensive analysis of glycoproteins is imperative for understanding glycoprotein bio-functions and identifying glycosylated biomarkers. However, the complexity and heterogeneity of glycan structures, coupled with the low abundance and poor ionization efficiencies of glycopeptides have all contributed to making the analysis and subsequent identification of glycans and glycopeptides much more challenging than any other biopolymers. Nevertheless, the significant advancements in enrichment techniques, chromatographic separation, and mass spectrometric methodologies represent promising avenues for mitigating these challenges. Numerous substrates and multifunctional materials are being designed for glycopeptide enrichment, proving valuable in glycomics and glycoproteomics. Mass spectrometry (MS) is pivotal for probing protein glycosylation, offering sensitivity and structural insight into glycopeptides and glycans. Additionally, enhanced MS-based glycopeptide characterization employs various separation techniques like liquid chromatography, capillary electrophoresis, and ion mobility. In this review, we highlight recent advances in enrichment methods and MS-based separation techniques for analyzing different types of protein glycosylation. This review also discusses various approaches employed for glycan release that facilitate the investigation of the glycosylation sites of the identified glycoproteins. Furthermore, numerous bioinformatics tools aiding in accurately characterizing glycan and glycopeptides are covered.