• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.51-58
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• 2013

As it is changing into aging society, rehabilitation, welfare and sports industry markets are being expanded fast. Especially, the field of vital signals interface to control welfare instruments like wheelchair, rehabilitation ones like an artificial arm and leg and general electronic ones is a new technology field in the future. Also, this technology can help not only the handicapped, the old and the weak and the rehabilitation patients but also the general public in various application field. The commercial bio-signal measurement instruments and interface systems are complicated, expensive and large-scaled. So, there are a lot of limitations for using in real life with ease. this thesis proposes a wireless transmission interface system that uses EMG(electromyogram) signals and a control module to manipulate hardware systems with portable size. We have designed a hardware module that receives the EMG signals occurring at the time of wrist movement and eliminated noises with filter and amplified the signals effectively. DSP(Digital Signal Processor) chip of TMS320F2808 which was supplied from TI company was used for converting into digital signals from measured EMG signals and digital filtering. We also have used PCA(Principal Component Analysis) technique and classified into four motions which have right, left, up and down direction. This data was transmitted by wireless module in order to display at PC monitor. As a result, the developed system obtains recognition success ratio above 85% for four different motions. If the recognition ratio will be increased with more experiments. this implemented system using EMG wrist direction signals could be used to control various hardware systems.

• Korean Journal of Food Science and Technology
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• v.42 no.2
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• pp.125-129
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• 2010

Isoflavone distribution and $\beta$-glucosidase activity in 16 commercially available doenjang samples were determined. Twelve of the samples were home-made doenjang (HMD) with a relatively long fermentation period and 4 of the samples were factory-produced doenjang (FPD) from four different manufactures. Total isoflavones (TI) in the HMD ranged from $370-723\;{\mu}g/g$, while those in FPD ranged from $179-537\;{\mu}g/g$. The isoflavone distribution in HMD was different from those in FPD. Generally, the TI in HMD was higher than those in FPD. The major isoflavone was aglycones, which ranged from 42.98 to 89.96% in HMD and from 35.51 to 93.48% in FPD. Isoflavones in the $\beta$-glucoside forms were not detected in tested FPD samples. Principal components analysis (PCA) of the isoflavone profiles showed that HMD were differentiated from FPD. First principal component (PC1) and second principal component (PC2) expressed 43.6 and 22.9% of the data variability, respectively. $\beta$-Glucosidase activity in doenjang was lower than that in raw soybeans. The results of this study can be used to understand the differences in the isoflavone distribution in traditionally manufactured and factory produced doenjang.

• Journal of the Korean earth science society
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• v.42 no.6
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• pp.623-631
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• 2021

The increased transport of marine hazardous and noxious substances (HNS) has resulted in frequent HNS spill accidents domestically and internationally. There are about 6,000 species of HNS internationally, and most of them have toxic properties. When an accidental HNS spill occurs, it can destroys the marine ecosystem and can damage life and property due to explosion and fire. Constructing a spectral library of HNS according to wavelength and developing a detection algorithm would help prepare for accidents. In this study, a ground HNS spill experiment was conducted in France. The toluene spectrum was determined through hyperspectral sensor measurements. HNS present in the hyperspectral images were detected by applying the spectral mixture algorithm. Preprocessing principal component analysis (PCA) removed noise and performed dimensional compression. The endmember spectra of toluene and seawater were extracted through the N-FINDR technique. By calculating the abundance fraction of toluene and seawater based on the spectrum, the detection accuracy of HNS in all pixels was presented as a probability. The probability was compared with radiance images at a wavelength of 418.15 nm to select abundance fractions with maximum detection accuracy. The accuracy exceeded 99% at a ratio of approximately 42%. Response to marine spills of HNS are presently impeded by the restricted access to the site because of high risk of exposure to toxic compounds. The present experimental and detection results could help estimate the area of contamination with HNS based on hyperspectral remote sensing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2287-2300
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• 2015

This study examined the spatio-temporal distributions of the phytoplankton community in the coastal waters of Gogunsan Islands (CoWGIs), West Sea of Korea, from January to September 2011. A total of 104 species of phytoplankton belonging to 56 genera were identified. This was low compared to the surrounding seas of the West Sea. In particular, diatoms and dinoflagellates comprised 60.5% and 34.6%, respectively, and it was most diverse in autumn. The standing crops fluctuated from $9.6{\times}10^4cells/L$ to $1.0{\times}10^7cells/L$. This was high in winter and summer and low in spring and autumn. The seasonal dominant species were Skeletonema costatum-like species, Thalassiosira nordenskioeldii, Dactyliosolen fragillisimus, and Chaetoceros debilis in winter, Guinardia delicatula in spring, Eucampia zodiacus, Cylindrotheca closterium, Ch. debilis, and Ch. curvisetus in summer, and S. costatum-like species, Ch. debilis, Ch. curvisetus, G. delicatula, and Leptocylindrus danicus in autumn. The total number of autochthonous and tychopelagic species was 39 species. This showed a 1/3 (33.3%) decrease compared to the 1980's. The chlorophyll a concentration fluctuated from $3.82{\mu}g/L$ in autumn to $13.36{\mu}g/L$ in summer. The bio-oceanographic characteristics of the CoWGIs based on principle component analysis (PCA) showed that it was dominated by the Saemangeum water mass in the high temperature season and by the Geum River water mass in the low temperature season. In other words, there has been a conversion to a closed inner bay followed by the dramatic progress of eutrophication, even in the CoWGIs after completion of the Saemangeum embankment.

• Korean Journal of Environmental Agriculture
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• v.34 no.2
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• pp.69-76
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• 2015

BACKGROUND: Contamination of soils by heavy metals is the serious environmental problem. In particular, industrial processing is one of the main sources of heavy metal contamination. The objective of this study was to investigate the distribution characteristics of heavy metals in soils collected from industrial complex. METHODS AND RESULTS: In this study, the soil contamination and enrichment factor (EF) of heavy metals were investigated in three national industrial complexes such as Yeosu, Ulsan and Sihwa Banwal industrial complexes. The target heavy metals includes Cd, Cu, As, Hg, Pb, Cr, Zn, and Ni. The results showed that the contents of Cd, Hg, Pb, Zn and Ni in Yeosu and the contents of Cu, As and Cr in Sihwa Banwal were higher than in any other industrial complex. The results of principal component analysis(PCA) in Yeosu, Ulsan and Sihwa Banwal complex could be explained up to approximately 81.4, 69.1 and 70.9% by two factor, respectively. Enrichment factors of Cd, Pb and Zn in all the investigated industrial complexes were above 1.0 that was the value judged to be a high contamination. And EF of Cr was above 1.0 in Sihwa Banwal complex. EF of Zn in all sites was generally high from the other heavy metals. CONCLUSION: Therefore, soils maybe significantly affected by heavy metals (especially, Cd, Pb and Zn) present in the emissions from industrial complexes.

• Journal of the Korean Geographical Society
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• v.38 no.4
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• pp.630-639
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• 2003

This study aims to analysis the distribution and change of cropland and forest, the Onseong, Saebyeol, and Eundeok counties on the lower reach of Duman(Tumen) river, northeast Korea, using 1992 year Landsat TM data, 2000 year Landsat ETM data, and digital terrain elevation data(DTED). Land cover and land use of the study areas are classified into cropland, forest, village, and water body, using the supervised classification method including 1:50,000 DTED analysis, image band composition, and principal component analysis(PCA). Results of quantitative analysis present that each growth rate of cropland of Onseong and Eundeok are 22.8% and 14.7% corresponding to decreasing rates of forest, 8% and 13.6% during 8 years from 1992 to 2000. In Onseong, Saebyeol, and Eundeok, each values of mean elevations and slope gradients increased to 192m, 95m, and 91m from 157m, 85m, and 78m, and to 6.6$^{\circ}$, 3.0$^{\circ}$, and 4.4$^{\circ}$ from 5.2$^{\circ}$, 2.5$^{\circ}$, and 3.0$^{\circ}$. Especially, in case of newly developed cropland, the values of mean elevation and mean gradient have 225m, 122m, and 127m, and 9.4$^{\circ}$, 5.1$^{\circ}$, and 8.0$^{\circ}$, in above three regions. These new croplands were developing along to deeper valleys and toward lower hill and mountain slope up to knickpoint zone of gradient change. Deforested lands for cropland have formed irregular pattern of patch-type, and become sources for the sheet erosion, rilling and gulleying in mountain slope and sedimentation in local river channel. Though there were no field checking, analysis using landsat images and GIS mapping can help understand actual environmental problems relating to cropland development of mountain slope in North Korea.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.4
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• pp.243-255
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• 2014

This study was investigated to estimate the relations between benthic environments and benthic polychaetous community from April 2012 to February 2013. Twenty four stations were selected sequentially with Seomjin River Estuary from the northern part of Gwangyang Bay. The study area could be divided into three characteristic zones based on salinity, water temperature, dissolved oxygen and pH such as Saline Water Zone (SWZ), Brackish Water Zone (BWZ), and Fresh Water Zone (FWZ). Salinity was above 30.0 psu in SWZ, drastically decreased toward inland in BWZ, and nearly zero psu in FWZ. SWZ showed its specific environmental characters like that water temperature fluctuated with little seasonal change and DO showed the lowest values among three zones, and pH maintained as consistent value without seasonal fluctuation. In FWZ, on the other hand, water temperature showed high seasonal fluctuation, DO showed the highest values among three zones, and pH fluctuated greatly. In sedimentary environment, mud, sand and sand/gravel were found as dominant sedimentary deposits in SWZ, BWZ and FWZ, respectively. Organic matter content and AVS in surface sediment were high in SWZ, while Chl-a content high in FWZ. This study area showed a marked environmental difference between FWZ and SWZ as follows: FWZ has coarse sediment and low salinity, low organic matter content, low AVS in FWZ but SWZ has fine sediment and high salinity, high organic matter content and AVS. Species number and mean density of benthic polychaete community was highest in Saline Water Zone (SWZ), drastically decreased in Brackish Water Zone (BWZ), and lowest in Fresh Water Zone (FWZ). Dominant polychates above 5.0% of individual numbers were 6 taxa. Lumbrineris longifolia, Prionospio cirrifera, Tharyx sp. occurred as main dominant species of all study periods, and Hediste sp., Praxillella affinis, Tylorrhynchus sp. dominantly occurred at some seasons. Inhabiting areas of dominant species were separated characteristically. Representative species in SWZ were Lumbrineris longifolia, Tharyx sp., Mediomastus sp.. Wide-appearing species between SWZ and BWZ were Prionospio cirrifera, Heteromastus filiformis, Aricidea sp.. Characteristic species in FWZ were Tylorrhynchus sp. and Hediste sp.. As the results of cluster analysis and nMDS based on the species composition of polychaetous community, unique station groups were established in SWZ and FWZ. Stations in BWZ were sub-divided into several groups with season. Pearson's correlation analysis and PCA between benthic environments and ecological characteristics of polychaetous community showed that salinity, sediment composition, organic content and dissolved oxygen played a role to determine the temporal and spatial distribution of the ecological characteristics as species number, mean density, abundance of main species, and ecological indices.

• Korean Journal of Heritage: History & Science
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• v.41 no.1
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• pp.5-19
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• 2008

Thirty six samples of Western asia glass vessel shards which were excavated from South Mound of Hwangnamdaechong were each measured for thickness, pore size and specific gravity and analyzed for ten major compositions and thirteen trace elements. The glass samples with colorless, greenish blue and dark purple blue were well classified by principal component analysis(PCA). All glass shards of Hwangnamdaechong belonged to Soda glass system ($Na_2O-CaO-SiO_2$) which have the range of 14~17% $Na_2O$ and 5~6% CaO. The corelation coefficients of (MgO, $K_2O$) and (MnO, CuO) showed above 0.90. The concentrations of thirteen trace elements apparently differentiated from colorless, greenish blue and dark blue glasses. We found that thirteen trace elements were very important indices for studying raw material of glass and the origin of glass making. Colorless glass : The specific gravity is $1.50{\pm}0.04$. Circle or oval circle pores are observed with regular direction in internal zone and the longest one is about 0.35 mm. The raw material of sodium must be the plant ash because sodium glasses contain HCLA(High CaO, Low $Al_2O_3$) and HMK(high MgO, high $K_2O$) and suggested to Sasanian glass. The total amount of coloring agent of colorless glass is below 1 % which is too small to attribute to the color. Greenish blue glass : The specific gravity is $1.58{\pm}0.04$. The fine pores which are 0.1~0.2mm are dispersed in internal zone. Sodium glasses are distributed to HCLA and HMK. Therefore the greenish blue glass also have used plant ash for raw material of sodium with the same as colorless glass. It was also suggested to the glass of Sasanian. The total amount of coloring agent of greenish blue glass is about 4% under the influence of working MnO, $Fe_2O_3$ and CuO. Dark purple blue glass : The specific gravity is $1.48{\pm}0.19$. There are rarely pores in internal zone. They are distributed to HCLA and LMK(Low MgO, Low $K_2O$) and suggested to Roman glass. The raw material of sodium is estimated to natron. The total amount of coloring agents of greenish blue is about 3% by $Fe_2O_3$ and CuO. These studies for western asia glass shards from South Mound of Hwangnamdaechong could be used in the future as the standard data which could be compared with those of other several graves in Korea and dispersed in foreign areas.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.58 no.4
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• pp.408-415
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• 2013

To better understand the morphological variation of the Perilla crop and their weedy types in East and Southeast Asia, we studied the morphological variation of 90 accessions by examining 10 morphological characteristics, such as flowering time, seed size, seed hardness, seed color, color of surface leaf, color of reverse side leaf etc. As a result, morphological variation determined that between cultivated var. frutescens and var. crispa, and between cultivated var. frutescens and its weedy type showed significant morphological differences in terms of seed size and seed hardness, whenever cultivated var. crispa and its weedy type could not showed significant differences in most morphological characters. In PCAs (principal component analysis), among 10 morphological characteristics, flower color (QL6), color of surface leaf (QL3), seed size (QN2), seed hardness (QL1), seed color (QL2), stem color (QL7), and color of reverse side leaf (QL4) contributed in negative direction on the first axis, while flowering time (QN1), leaf shape (QL5), and degree of pubescence (QL8) contributed in positive direction on the first axis. Among these morphological characters, particularly flower color (QL6), color of surface leaf (QL3), seed size (QN2), seed hardness (QL1), and degree of pubescence (QL8) were useful characters for discrimination between cultivated var. frutescens and weedy var. crispa, and between cultivated var. frutescens and its weedy type. However, most accession of cultivated and weedy types of var. crispa was not clearly discriminated by PCA analyses. Although the wild ancestral species of var. frutescens and of var. crispa are still unknown in East and Southeast Asia, the weedy types of Perilla crop may be the key taxon for our understanding of the origin of cultivated types of var. frutescens and var. crispa.