• Journal of the Korean Geotechnical Society
• /
• v.23 no.4
• /
• pp.15-24
• /
• 2007

Two kinds of temperature monitoring technology have been introduced in this study, which can measure coincidently temperatures at many points along a single length of cable. One is to use a thermal sensor cable comprizing of addressable thermal sensors. The other is to use an optic fiber sensor with Distributed Temperature Sensing (DTS) system. The differences between two technologies can be summarized as follows: A thermal sensor cable has a concept of "point sensing" that can measure temperature only at a predefined position. The accuracy and resolution of temperature measurement are up to the capability of the individual thermal sensor. On the other hand, an optic fiber sensor has a concept of "distributed sensing" because temperature is measured practically at all points along the fiber optic cable by analysing the intensity of Raman back-scattering when a laser pulse travels along the fiber. Thus, the temperature resolution depends on the measuring distance, measuring time and spatial resolution. The purpose of this study is to investigate the applicability of two different temperature monitoring techniques in technical and economical sense. To this end, diverse experiments with two techniques were performed and two techniques are applied under the same condition. Considering the results, the thermal sensor cable will be well applicable to the assessment of groundwater flow, geothermal distribution and grouting efficiency within about loom distance, and the optic fiber sensor will be suitable for long distance such as pipe line inspection, tunnel fire detection and power line monitoring etc.

• Journal of the Korean Geotechnical Society
• /
• v.24 no.9
• /
• pp.41-48
• /
• 2008

The aim of this study is to present a graphical method in order to evaluate stages in shrinkage cracking. Firstly, the distribution of crack openings is established by sorting the openings of individual cracks in the soil cracking system. Secondly, it is normalized in a range of 0 to 1 to obtain the normalized crack opening distribution. Thirdly, three S-shape curve models introduced by Brooks and Corey(1964), Fredlund and Xing(1994) and van Genuchten(1980) are chosen to fit the normalized crack opening distribution using a curve fitting method. The accuracy of fitting which is described through fitting parameters by the van Genuchten equation is much higher than that by the Brooks and Corey equation and slightly higher than that by the Fredlund and Xing equation; thus the van Genuchten model is used. Finally, the stages of shrinkage cracking are graphically evaluated by drawing three separate straight lines corresponding to three linear parts of the fitted normalized crack opening distribution. The proposed method is tested with different sample thicknesses. The measured data are fitted by the selected model with the fairly high regression coefficient and small root mean square error. The results show graphically that shrinkage cracking comprises three stages; namely, primary, secondary and residual stages. Subsequently, the ranges of evaluated crack opening for each of these stages are presented.

• Korean Journal of Animal Reproduction
• /
• v.9 no.2
• /
• pp.133-139
• /
• 1985

An immunoassay may be defined as an analytical procedure involving the competitive reaction between a limiting concentration of specific antibody and two populations of antigen, one of which is labelled or immobillized. The advent of immunoassay has revolutionised our knowledge of reproductive physiology and the practice of veterinary and clinical medicine. Radioimmunoassay (RIA) was the first of these methods to be developed, which meausred the analyte with good sensitivity, accuracy and precision (1,2). The essential components of RIA are:-(i) a limited concentration of antibodies, (ii) a reference preparation, and (iii) an antigen labelled with a radioisotope (usually tritium or iodine-125). Most procedures invelove isolating the antibody-bound fraction and measuring the amount of labelled antigen. Good facilities are available for scintilltion counting, data reduction nd statistical analysis. RIA is undergoing refinement through:-(i) the introduction of new techniques to separate the antibody-bound and free fractions which minimize the misclassification of labelled antigen into these compartments, and the amount of non-specfic binding. (3), (ii) the development of non-extration for the measurement of haptens (4), (iii) the determination of a, pp.rent free (i.e. non-protein bound) analytes (5), and (iv) the use of monoclonal antibodies(6). In 1968, Miles and Hales introduced in important new type of immunoassay which they termed immunora-diometric assay (IRMA) based on t도 use of isotopically labelled specific antibodies(7) in a move from limited to excess reagent systems. The concept of two-site IRMAs (with a capture antibody on a solid-phase, and a second labelled antibody to a different antigenic determinant of the analyte) has enabled the development of more sensitive and less-time consuming methods for the measurement of protein hormones ovar wide concentration of analyte (8). The increasing use of isotopic methos for diverse a, pp.ications has exposed several problems. For example, the radioactive half-life and radiolysis of the labelled reagent limits assay sensitivity and imposes a time limit on the usefulness of a kit. In addition, the potential health hazards associated with the use and disposal of radioactive cmpounds and the solvents and photofluors necessary for liquid scientillation counting are incompatable with the development of extra-laboratory tests. To date, the most practical alternative labels to radioisotopes, for the measurement of analytes in a concentration > 1 ng/ml, are erythrocytes, polystyrene particiles, gold sols, dyes and enzymes or cofactors with a visual or colorimetric end-point(9). Increased sensitivity to<1 pg/ml may be obtained with fluorescent and chemiluminescent labels, or enzymes with a fluorometric, chemiluminometric or bioluminometric end-point. The sensitivity of any immunoassay or immunometric assay depends on the affinity of the antibody-antigen reaction, the specific activity of the label, the precision with which the reagents are manipulated and the nonspecific background signal (10). The sensitivity of a limited reagent system for the measurement of haptens or proteins is mainly dependent upon the affinity of the antibodies and the smalleest amount of reagent that may be manipulated. Consequently, it is difficult in practice to improve on the sensitivity obtained with iodine-125 as the label. Conversely, with excess reagent systems for the measurement of proteins it is theoretically possible to increase assay sensitivity at least 1000 fold with alternative luminescent labels. To date, a 10-fold improvement has been achieved, and attempts are being made to reduce the influence of other variables on the specific signal from the immunoreaction.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.37-64
• /
• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.197-216
• /
• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.6
• /
• pp.897-906
• /
• 2017

Projection Mapping, also known as Spatial Augmented Reality(SAR) has attracted much attention recently and used for many division, which can augment physical objects with projected various virtual replications. However, conventional approaches towards projection mapping have faced some limitations. Target objects' geometric transformation property does not considered, and movements of flexible objects-like paper are hard to handle, such as folding and bending as natural interaction. Also, precise registration and tracking has been a cumbersome process in the past. While there have been many researches on Projection Mapping on static objects, dynamic projection mapping that can keep tracking of a moving flexible target and aligning the projection at interactive level is still a challenge. Therefore, this paper propose a new method using Unity3D and ARToolkit for high-speed robust tracking and dynamic projection mapping onto non-rigid deforming objects rapidly and interactively. The method consists of four stages, forming cubic bezier surface, process of rendering transformation values, multiple marker recognition and tracking, and webcam real time-lapse imaging. Users can fold, curve, bend and twist to make interaction. This method can achieve three high-quality results. First, the system can detect the strong deformation of objects. Second, it reduces the occlusion error which reduces the misalignment between the target object and the projected video. Lastly, the accuracy and the robustness of this method can make result values to be projected exactly onto the target object in real-time with high-speed and precise transformation tracking.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.9
• /
• pp.265-273
• /
• 2018

In this paper, we propose an acoustic event detection method in tunnels using non-negative tensor factorization (NTF) and hidden Markov model (HMM) applied to multi-channel audio signals. Incidents in tunnel are inherent to the system and occur unavoidably with known probability. Incidents can easily happen minor accidents and extend right through to major disaster. Most incident detection systems deploy visual incident detection (VID) systems that often cause false alarms due to various constraints such as night obstacles and a limit of viewing angle. To this end, the proposed method first tries to separate and detect every acoustic event, which is assumed to be an in-tunnel incident, from noisy acoustic signals by using an NTF technique. Then, maximum likelihood estimation using Gaussian mixture model (GMM)-HMMs is carried out to verify whether or not each detected event is an actual incident. Performance evaluation shows that the proposed method operates in real time and achieves high detection accuracy under simulated tunnel conditions.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.241-265
• /
• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.285-301
• /
• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.23-34
• /
• 2023

Wearable technology for military applications has received considerable attention as a means of personal status check and monitoring. Among many, an implementation to recognize specific motion states of a human is promising in that allows active management of troops by immediately collecting the operational status and movement status of individual soldiers. In this study, as an extension of military wearable application research, a new ankle wearable device is proposed that can glean the information of a soldier on the battlefield on which action he/she takes in which environment. Presuming a virtual situation, the soldier's upper limbs are easily exposed to uncertainties about circumstances. Therefore, a sensing module is attached to the ankle of the soldier that may always interact with the ground. The obtained data comprises 3-axis accelerations and 3-axis rotational velocities, which cannot be interpreted by hand-made algorithms. In this study, to discern the behavioral characteristics of a human using these dynamic data, a data-driven model is introduced; four features extracted from sliced data (minimum, maximum, mean, and standard deviation) are utilized as an input of the model to learn and classify eight primary military movements (Sitting, Standing, Walking, Running, Ascending, Descending, Low Crawl, and High Crawl). As a result, the proposed device could recognize a movement status of a solider with 95.16% accuracy in an arbitrary test situation. This research is meaningful since an effective way of motion recognition has been introduced that can be furtherly extended to various military applications by incorporating wearable technology and artificial intelligence.