• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.35 no.4
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• pp.8-16
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• 2003

The objective of this study was to investigate the potential application of the separated refining system in the papermaking process using cotton pulps. The cotton staple and linter fibers were expected to show a great difference in their refining responses due to their morphological and physical differences. Experiments were conducted to examine the differences in flocculation tendency, CED viscosity, fiber length, handsheet properties and the SEM surface images between staple and linter fibers at a given refining degree. These fibers were also subjected to separated refining in a laboratory-scale beater and in a mill-scale refiner as well. The effect of the separated refining on the refining rates and papermaking properties were evaluated. Results obtained are summarized as follows: 1. Fiber flocculation tendency of cotton staple was estimated to be significantly greater than that of linter fibers; 2. The staple fibers showed higher cellulose DP, longer fiber length and higher sheet strength at a given refining degree compared to linter fibers, but remarkably slower refining rate was observed; 3. The separated refining system exhibited a significant increase in sheet strengths, especiauy in folding endurance, with an increase in the fibrillation on the surface of staple fibers, but slightly lower or comparable fiber length after refining to the mixed refining system; 4. Similar results were also obtained from the machine trial in which about 7-8％ energy saving effects were achived in the separated refining system. On the basis of the results observed in this study, it was concluded that a significant increase in paper strength and a substantial reduction in refining energy consumption could be achieved using the separated refining system for the cotton staple and linter fiber stock refining.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.675-680
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.

• Journal of Radiation Industry
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• v.5 no.2
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• pp.137-143
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• 2011

Epoxy resins are widely used as high performance thermosets in many industrial applications, such as coatings, adhesives and composites. Recently, a lot of research has been carried out in order to improve their mechanical properties and thermal stability in various fields. Carbon nanotubes possess high physical and mechanical properties that are considered to be ideal reinforcing materials in composites. CNT-reinforced epoxy system hold the promise of delivering superior composite materials with their high strength, light weight and multi functional features. Therefore, this study used multi-walled carbon nanotubes (MWNT) and gamma rays to improve the mechanical and thermal properties of epoxy. The diglycidyl ether of bisphenol A (DGEBA) as epoxy resins were cured by gamma ray irradiation with well-dispersed MWNTs as a reinforcing agent and triarylsulfonium hexafluoroantimonate (TASHFA) as an initiator. The flexural modulus was measured by UTM (universal testing machine). At this point, the flexural modulus factor exhibits an upper limit at 0.1 wt% MWNT. The thermal properties had improved by increasing the content of MWNT in the result of TGA (thermogravimetric analysis). However, they were decreased with increasing the radiation dose. The change of glass transition temperature by the radiation dose was characterized by DMA (dynamic mechanical analysis).

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.196-201
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• 2019

The purpose of this study is to identify the effects of the apply of lumbar stabilization exercises and lumbar strengthening exercises using Medx machine on back functions such as static balance, lumbar reposition sense, and back pain(VAS) in chronic low back pain. This study divided 30 chronic low back pain patients who experienced only lumbar back pain(male: 5, female: 10) into a lumbar stabilization exercise group and a complex exercise group, and then performed their respective exercise programs for three times a week over a 8-week period. The lumbar static balance, lumbar reposition sense, and lumbar back pain were measured using a pair t-test within each group, and were also compared between two groups using an independent t-test. The results of the present study were as follows: Both groups exhibited statisti cally significant increases after performing their own exercise program in the static balance, lumbar reposition sense, and lumbar back pain(p<.05). However, the comparison of two groups confirmed that the complex exercise group resulted in greater effects than the lumbar stabilization exercise group(p<.05). In conclusion, muscle strengthening and stabilization exercises in chronic low back pain patients are considered to not only relieve back pain, but also improve various back functions.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.98-105
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• 2021

The sensory stimulation of a cosmetic product has been deemed to be an ancillary aspect until a decade ago. That point of view has drastically changed on different levels in just a decade. Nowadays cosmetic formulators should unavoidably meet the needs of consumers who want sensory satisfaction, although they do not have much time for new product development. The selection of new products from candidate products largely depend on the panel of human sensory experts. As new product development cycle time decreases, the formulators wanted to find systematic tools that are required to filter candidate products into a short list. Traditional statistical analysis on most physical property tests for the products including tribology tests and rheology tests, do not give any sound foundation for filtering candidate products. In this paper, we suggest a deep learning-based analysis method to identify hand cream products by raw electric signals from tribological sliding test. We compare the result of the deep learning-based method using raw data as input with the results of several machine learning-based analysis methods using manually extracted features as input. Among them, ResNet that is a deep learning model proved to be the best method to identify hand cream used in the test. According to our search in the scientific reported papers, this is the first attempt for predicting test cosmetic product with only raw time-series friction data without any manual feature extraction. Automatic product identification capability without manually extracted features can be used to narrow down the list of the newly developed candidate products.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.1
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• pp.86-98
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• 2019

Recently, the trend of building container-based PaaS (Platform-as-a-Service) is expanding. Container-based platform technology has been a core technology for realizing a PaaS. Containers have lower operating overhead than virtual machines, so hundreds or thousands of containers can be run on a single physical machine. However, recording and monitoring the storage logs for a large number of containers running in cloud computing environment occurs significant overhead. This work has identified two problems that occur when detecting a file system change event of a container running in a cloud computing environment. This work also proposes a system for container file system event detection in the environment by solving the problem. In the performance evaluation, this work performed three experiments on the performance of the proposed system. It has been experimentally proved that the proposed monitoring system has only a very small effect on the CPU, memory read and write, and disk read and write speeds of the container.