• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.64-70
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• 2011

Portable artificial electronic nose (E-nose) system suffers from noisy fluctuation in surroundings such as temperature, vapor concentration, and gas flow, because its measuring condition is not controled precisely as in the laboratory. It is important to develop a simple and robust vapor recognition technique applicable to this uncontrolled measurement, especially for the portable measuring and diagnostic system which are expanding its area with the improvements in micro bio sensor technology. This study used a PDA-based portable E-nose to collect the uncontrolled vapor measurement signals, and applied the image matching algorithm developed in the previous study on the measured signal to verify its robustness and improved accuracy in portable vapor recognition. The results showed not only its consistent performance under noisy fluctuation in the portable measurement signal, but also an advanced recognition accuracy for 2 similar vapor species which have been hard to discriminate with the conventional maximum sensitivity feature extraction method. The proposed method can be easily applied to the data processing of the ubiquitous sensor network (USN) which are usually exposed to various operating conditions. Furthermore, it will greatly help to realize portable medical diagnostic and environment monitoring system with its robust performance and high accuracy.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.32-38
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• 2007

A batch Process has a multi-way data structure that consists of batch-time-variable axis, so the statistical modeling of a batch process is a difficult and challenging issue to the process engineers. In this study, We applied a statistical process control technique to the general batch process data, and implemented a fault-detection and Statistical process control system that was able to detect, identify and diagnose the fault. Semiconductor etch process and semi-batch styrene-butadiene rubber process data are used to case study. Before the modeling, we pre-processed the data using the multi-way unfolding technique to decompose the data structure. Multivariate regression techniques like support vector regression and partial least squares were used to identify the relation between the process variables and process condition. Finally, we constructed the root mean squared error chart and variable contribution chart to diagnose the faults.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.6
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• pp.251-256
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• 2019

A capsule endoscopy is a medical device that can capture an entire digestive organ from the esophagus to the anus at one time. It produces a vast amount of images consisted of about 8~12 hours in length and more than 50,000 frames on a single examination. However, since the analysis of endoscopic images is performed manually by a medical imaging specialist, the automation requirements of the analysis are increasing to assist diagnosis of the disease in the image. Among them, this study focused on automatic detection of polyp images. A polyp is a protruding lesion that can be found in the gastrointestinal tract. In this paper, we propose a weighted-image generation method to enhance the polyp image learning by multi-scale analysis. It is a way to extract the suspicious region of the polyp through the multi-scale analysis and combine it with the original image to generate a weighted image, that can enhance the polyp image learning. We experimented with SVM and RF which is one of the machine learning methods for 452 pieces of collected data. The F1-score of detecting the polyp with only original images was 89.3%, but when combined with the weighted images generated by the proposed method, the F1-score was improved to about 93.1%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.33-39
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• 2015

Recently, the event of slope failure has been occurring frequently due to rapid climate changes and broad development of infrastructures, and the research for establishment of monitoring and prevention system has been an attentive issue. The major influence factors of slope failure mechanism can be considered moisture and temperature in soil, and the slope failure can be monitored and predicted through the trend of moisture-temperature change. Therefore, the combined sensing technology for the continuous measurement of moisture-temperature with different soil depths is needed for the slope monitoring system. The various independent sensors for each item (i.e. temperature and moisture respectively) have been developed, however, the research for development of combined sensing system has been hardly carried out. In this study, the high-fidelity sensor combing temperature-moisture measurement by using the minimized current consuming temperature circuit and the microwave emission moisture sensor is developed and applied on the slope failure monitoring system. The feasibility of developed monitoring system is verified by various experimental approaches such as standard performance test, mockup test and long-term field test. As a result, the developed temperature-moisture combined measurement system is verified to be measuring and monitoring the temperature and moisture in soil accurately.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.1-11
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• 2024

The purpose of this paper is to suggest a method for applying a reasonable dam axial seismic load loading method and load-bearing capacity evaluation method in the dynamic analysis of the pier part of a concrete dam to which the seismic force of the collapse prevention level is applied. To this end, the pier part of a concrete dam was selected as a target facility, and the characteristics of the dynamic behavior in the axial direction of the weir dam were analyzed through dynamic analysis applying various weir widths, and 'U.S. The load-bearing capacity evaluation was performed by applying the RC hydraulic structure evaluation technique suggested by the Army Corps, 2007'. As a result of the study, when applying seismic force in the axial direction of the pier part, it is more realistic to assume that the axial direction of the weir part dam behaves as a rigid body and 'U.S. Army Corps, 2007' suggested that the method of reviewing the load-bearing capacity for moment and shear was considered reasonable, so it was concluded that improvement of the current evaluation method was necessary. If the improvement of the research result is applied, it will have the effect of deriving more reasonable evaluation results than the current seismic performance evaluation method using CLE. It is judged that additional research is needed in the future on the torsional moment occurring in the pier part.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.6
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• pp.175-181
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• 2022

The condition of infrastructure deteriorates as the service life increases. Since most infrastructure in South Korea were intensively built during the period of economic growth, the proportion of outdated infrastructure is rapidly increasing now. Aging of such infrastructure can lead to safety accidents and even human casualties. To prevent these issues in advance, periodic and accurate inspection is essential. For this reason, the need for research to detect various types of damage using computer vision and deep learning is increasingly required in the field of remotely controlled or autonomous inspection. To this end, this study proposed a neural network structure that can detect concrete damage by classifying it into three types. In particular, the proposed neural network can detect them more accurately through a hierarchical learning technique. This neural network was trained with 2,026 damage images and tested with 508 damage images. As a result, we completed an algorithm with average mean intersection over union of 67.04% and F1 score of 52.65%. It is expected that the proposed damage detection algorithm could apply to accurate facility condition diagnosis in the near future.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.585-590
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• 2024

In this study, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of engineers and simple measuring tools. With the recent enactment of the Track Diagnosis Act, a large budget has been invested and the volume of rail diagnosis is rapidly increasing, but it is difficult to secure the reliability of diagnosis results using labor-intensive visual inspection techniques. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the rail internal crack characteristics due to rail surface damage were studied. In field measurements, rail surface damage locations were selected, samples of various damage types were collected, and the rail surface damage status was evaluated. In indoor testing, we intend to analyze the correlation between rail surface defects and internal defects using a electron scanning microscope (SEM). To determine the crack growth rate of urban railway rails currently in use, the Gaussian probability density function was applied and analyzed.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.114-125
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• 2009

In this study, a design value analysis technique that considered stochastic LCC and stochastic performance evaluation was proposed, and by introducing the concept of reliability analysis, a decision making that secured reliability was supported. The results of this study, which was carried out according to the above objectives and methods, are summarized as follows: 1) The design value analysis procedures and value state function, improved in order to carry out a reliable analysis when evaluating alternate proposals that were extracted after the function definition was complete, were formalized, and in order to secure consistency and efficiency for value evaluation procedures, an evaluation index scheme was proposed; 2) Database collection and analysis were done for a bridge's LCC analysis. As for the collection scope of data, literature of previous research done on a bridge's LCC analysis was used as the basis for analysis, and for securing reliability regarding analysis results and dealing with uncertainty of collected data, the MCS technique was applied; 3) Weights and evaluation ranks for performance evaluation of each of the alternate proposals, as well as LCC analysis model, analysis period, discount rate, user expense, safety inspection and safety diagnosis expense conditions for LCC analysis were proposed. Lastly, a feasibility study was done and conclusion was made about "OO grand bridge and connecting road construction work execution design" project centered on value analysis execution case.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.