• Asia pacific journal of information systems
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• v.30 no.3
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.1
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• pp.39-46
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• 2012

In this paper, for the cloud system by explaining how the forensic aspects of the investigation. Smartphone Growth Entering a variety of applications were developed which cloud systems of personal information and information assets sharing applications as during incidents on the case evidence collection, an important factor, whereas such systematic investigative methods, born in the course of my investigation of the can be confusing. This paper on the forensic aspects of the cloud system by proposing a crime scene investigation procedures, investigative support, and aiding in the systematic collection of data to support evidence.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.679-687
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• 2019

This paper presents the autonomous swimming technology developed for an Autonomous Underwater Vehicle (AUV) operating in the underwater jacket structure environment. To prevent the position divergence of the inertial navigation system constructed for the primary navigation solution for the vehicle, we've developed kinds of marker-recognition based underwater localization methods using both of optical and acoustic cameras. However, these two methods all require the artificial markers to be located near to the cameras mounted on the vehicle. Therefore, in the case of the vehicle far away from the structure where the markers are usually mounted on, we may need alternative position-aiding solution to guarantee the navigation accuracy. For this purpose, we develop a sonar image processing based underwater localization method using a Forward Looking Sonar (FLS) mounted in front of the vehicle. The primary purpose of this FLS is to detect the obstacles in front of the vehicle. According to the detected obstacle(s), we apply an Occupancy Grid Map (OGM) based path planning algorithm to derive an obstacle collision-free reference path. Experimental studies are carried out in the water tank and also in the Pohang Yeongilman port sea environment to demonstrate the effectiveness of the proposed autonomous swimming technology.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.53-53
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• 2008

Piezoelectric transformers have been widely used such as DC-DC convertor, invertor, Ballast, etc. Because, the y have some merits compared with electro-magnetic transformers such as step-up ratio, high efficiency, small size and lg hit weight, etc. Piezoelectric transformer require high electromechanical coupling factor kp in order to induce a large out put power in proportional to applied electric field. And also, high mechanical quality factor Qm is required to prevent mechanical loss and heat generation. In general, PZT system ceramics should be sintered at high temperatures between 1200 and $1300^{\circ}C$ in order to obtain complete densification. Accordingly, environmental pollution due to its PbO evaporation. Hence, to reduce its sintering temperature, various kinds of material processing methods such as hot pressing, high energy mill, liquid phase sintering, and using ultra fine powder have been performed. Among these methods, liquid phase sintering is basically an effective method for aiding densification at low temperature. In this study, In order to comparis on low temperature sintering and solid state sintering piezoelectric transformers, rosen type transformers were fabricated u sing two PZT ceramics compositions and their electrical properties were investigated.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.371-393
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• 2023

Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious, expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, and deep learning methods. Specifically, it will provide a comparative analysis of crack detection methods using deep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarks commonly used in deep learning research. Evaluation metrics employed to check the performance of different models are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study provides an in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novel architectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this study provides a summary of the key insights gained from the comparative analysis, highlighting the potential of deep learning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuable resource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiring further advancements in this domain.

• The Journal of Korean Physical Therapy
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• v.22 no.3
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• pp.9-15
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• 2010

Purpose: To evaluate, in patients with degenerative disc disease (DDD), the efficacy of using spinal stabilizing exercises for the reversal? of atrophy of the multifidus and psoas major, reductions in pain and disability, and for increases in paraspinal muscle strength. Methods: Nineteen patients diagnosed with DDD participated for 10 weeks in a spinal stabilization exercise program. Pain and disability were measured before and after exercise using, respectively, a visual analogue scale (VAS) and the Oswestry Disability Index (ODI). Paraspinal muscular strength in four directions was evaluated using CENTAUR. Both before and after exercise we used computed tomography (CT) too measure cross-sectional areas (CSAs) of both the left and right multifidus and the psoas major at the upper & lower endplate of L4. Results: After 10 weeks of a spinal stabilization exercise program, pain was significantly decreased from $5.7{\pm}0.9$ to $2.5{\pm}0.9$ (p<0.01); the ODI score decreased from $16.7{\pm}4.9$ to $7.3{\pm}3.1$. Paraspinal muscle strength was significantly increased (p<0.01) and the CSAs of the left and right multifidus and psoas major muscles were significantly increased (p<0.01). Conclusion: Spinal stabilization exercise is effective in reversing atrophy in DDD patients, in reducing pain and disability, and in increasing paraspinal muscle strength. It is an effective treatment foro aiding rehabilitation in these cases.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.9
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• pp.1525-1534
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• 2018

Objective: The aim of this study was to examine the use of outcome-based observations within Assured Dairy Farm scheme (ADF), Soil Association Organic Standards (SA), and cross compliance (CC) farm assessment reports. Methods: A total of 449 ADF reports, 37 SA reports and 26 CC reports were analyzed and their objective comments categorized as either resource-based or outcome-based. Results: A mean of 61.0% of ADF questions were responded to with comments, in comparison to 25.0% of SA and, 21.0% of CC report questions. The SA and CC reports had significantly more outcome-based comments than the ADF (p<0.001). The assessors' tendency of choosing resource-based approach was revealed in the questionnaire results. Conclusion: Generally, the comments were comprehensive and contained professional judgements. Large numbers of comments provided in the ADF reports were mostly compliant and resource-based evidence, which serves as proof of assessment rather than aiding the certifying process. The inclusion of specific welfare outcome measures in the SA inspection likely increased the use of outcome-based comments in the reports, irrespective of whether the farm achieved compliance with a given standards. The CC scheme, on the other hand, focused on providing outcome-based evidence to justify noncompliant decisions.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.161-176
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• 2005

In a situation that ordinal preferences on multiattribute weights are captured, we present two solution approaches: an exact approach and an approximate method. The former, an exact solution approach via interaction with a decision-maker, pursues the progressive reduction of a set of non-dominated alternatives by narrowing down the feasible attribute weights region. Subsequent interactive questions and responses, however, sometimes may not guarantee the best alternative or a complete rank order of a set of alternatives that the decision-maker desires to have. Approximate solution approaches, on the other hand, can be divided into three categories including surrogate weights methods, dominance value-based decision rules, and three classical decision rules. Their efficacies are evaluated in terms of choice accuracy via a simulation analysis. The simulation results indicate that a proposed hybrid approach, intended to combine an exact solution approach through interaction and a dominance value-based approach, is recommendable for aiding a decision making in a case that a final choice is seldom made at single step under attribute weights that are imprecisely specified beyond ordinal descriptions.

• 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.

• Journal of Information Technology Applications and Management
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• v.31 no.2
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• pp.35-49
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• 2024

With the development of e-commerce, live streaming e-commerce, as an emerging marketing method, is on the rise. It integrates various ways of information delivery, providing consumers with unprecedented shopping experiences, particularly through its interactive nature, which can increase audience engagement and immersion. This study delves into how interactive elements in live streaming e-commerce influence consumer emotions and purchase intentions. By employing literature review and empirical analysis methods, we analyzed various interactive factors in the live streaming e-commerce environment and revealed the process through which these factors stimulate audience emotions and lead to specific purchasing behaviors. The results confirm that the interactive appeal of live streaming e-commerce significantly influences consumers' positive emotional responses, consequently enhancing purchase intentions. This study aims to explore the relationship between the interactive features of live streaming e-commerce and consumer emotional responses and purchase intentions, thereby filling theoretical gaps in the field of live streaming e-commerce and proposing new marketing theories. Additionally, by analyzing how interactive features stimulate consumers, optimal live content strategies can be proposed for live streaming e-commerce platforms and hosts, thus aiding in the improvement of marketing strategies and sales effectiveness.