• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.1
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• pp.1-13
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• 2000

Electronic Data Interchange (EDI) has a significant impact on business practices by eliminating paper related adult trails and enabling transactions to be processed at high speed without human intervention. Major advantages advangtages and benefits derived from EDI, however, depend upon the usage of EDI controls. Management must determine whether their investment on EDI controls is appropriate, as the establishment of EDI controls demandds much resources and high skills. This study proposes data envelopment analysis model to identify efficient and inefficient EDI control systems in various context of input (formal and automated EDI controls) and output (EDI implementation and performance). DEA can also determine the factors that are significantly different between efficient and inefficient groups. The model is tested using data collected form EDI adopters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.201-203
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• 2011

We report an experimental result of applying a data mining algorithm for analyzing the questionnaire results of high school students' views on science-technology-society (HS-VOSTS). The preliminary empirical result of Naive Bayes classifier on HS-VOSTS questionnaire from one South Korean university students indicates that data mining algorithms can be effectively applied to automated knowledge discovery from students' survey data.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• KSTLE International Journal
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• v.1 no.1
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• pp.34-42
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• 2000

A method and results of classification of four different metallic wear debris were presented by using their color features. The color image of wear debris was used far the initial data, and the color properties of the debris were specified by HSI color model. Particles were characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used fer the definition of a classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• Smart Structures and Systems
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• v.16 no.3
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• pp.435-457
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• 2015

The most widely known form of multifunctional aircraft structure is smart structures for structural health monitoring (SHM). The aim is to provide automated systems whose purposes are to identify and to characterize possible damage within structures by using a network of actuators and sensors. Unfortunately, environmental and operational variability render many of the proposed damage detection methods difficult to successfully be applied. In this paper, an original robust damage detection approach using output-only vibration data is proposed. It is based on independent component analysis and matrix perturbation analysis, where an analytical threshold is proposed to get rid of statistical assumptions usually performed in damage detection approach. The effectiveness of the proposed SHM method is demonstrated numerically using finite element simulations and experimentally through a conformal load-bearing antenna structure and composite plates instrumented with piezoelectric ceramic materials.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.319-327
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• 2022

Robots for construction sites, although not deeply widespread, are finding applications in the duties of project monitoring, material movement, documentation, security, and simple repetitive construction-related tasks. A significant shortcoming in the use of robots is the complexity involved in programming and re-programming an automation routine. Robotic programming is not an expected skill set of the traditional construction industry professional. Therefore, this research seeks to deliver a low-cost approach toward re-programming that does not involve a programmer's skill set. The researchers in this study examined an approach toward programming a terrestrial-based drone so that it follows a taped path. By doing so, if an alternative path is required, programmers would not be needed to re-program any part of the automated routine. Changing the path of the drone simply requires removing the tape and placing a different path - ideally simplifying the process and quickly allowing practitioners to implement a new automated routine. Python programming scripts were used with a DJI Robomaster EP Core drone, and a terrain navigation assessment was conducted. The study examined the pass/fail rates for a series of trial run over different terrains. The analysis of this data along with video recording for each trial run allowed the researchers to conclude that the accuracy of the tape follow technique was predictable on each of the terrain surfaces. The accuracy and predictability inform a non-coding construction practitioner of the optimal placement of the taped path. This paper further presents limitations and suggestions for some possible extended research options for this study.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.146-154
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• 2019

Recently, a system has been developed and applied to establish and utilize HD maps through R&D. The biggest problem, however, is the lack of a proper HD map update system, which requires the development and adoption of such a system as soon as possible. In addition, in the case of updating HD maps for automated driving, integrity and accuracy of maps are required for safe driving, so an test of these technologies and data quality is required. In April 2018, the Ministry of Land, Infrastructure and Transport implemented a project to 'Develop Technology to Demonstrate and Share the Instant Road Change Detection and Update Technology for automated driving. This paper analyzed the technology for updating map based on the investigation and analysis of relevant technology trends for the development of integrated demonstration and sharing technology of road change rapid detection and updating map technology, and put forward the criteria for road change rapid detection, integrated quality verification of update technology. It is expected that the results of this study will contribute to quality assurance of HD maps that support safety driving for automated vehicles.