• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2393-2399
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• 2013

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.853-862
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• 2002

Face Detection can be defined as follows : Given a digitalized arbitrary or image sequence, the goal of face detection is to determine whether or not there is any human face in the image, and if present, return its location, direction, size, and so on. This technique is based on many applications such face recognition facial expression, head gesture and so on, and is one of important qualify factors. But face in an given image is considerably difficult because facial expression, pose, facial size, light conditions and so on change the overall appearance of faces, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact face detection which overcomes some restrictions by using neural network. The proposed system can be face detection irrelevant to facial expression, background and pose rapidily. For this. face detection is performed by neural network and detection response time is shortened by reducing search region and decreasing calculation time of neural network. Reduced search region is accomplished by using skin color segment and frame difference. And neural network calculation time is decreased by reducing input vector sire of neural network. Principle Component Analysis (PCA) can reduce the dimension of data. Also, pose estimates in extracted facial image and eye region is located. This result enables to us more informations about face. The experiment measured success rate and process time using the Squared Mahalanobis distance. Both of still images and sequence images was experimented and in case of skin color segment, the result shows different success rate whether or not camera setting. Pose estimation experiments was carried out under same conditions and existence or nonexistence glasses shows different result in eye region detection. The experiment results show satisfactory detection rate and process time for real time system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.441-453
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• 2017

This paper described the spatio-temporal distributions in the phytoplankton community, such as species composition, standing crops, and dominant species, from July 2012 to April 2013 in the Coastal Waters of Central South Sea (CWoCSS) of Korea. A total of 87 species of phytoplankton belonging to 52 genera were identified. In particular, diatoms and phytoflagellates comprised more than 62.1% and 37.9% of the total species, respectively. The phytoplankton cell density fluctuated with an annual mean of $7.9{\times}10^4cells{\cdot}L^{-1}$ between the lowest value of $1.0{\times}10^3cells{\cdot}L^{-1}$ in spring and the highest value of $4.5{\times}10^5cells{\cdot}L^{-1}$ in winter. The seasonal succession of the dominant species were Chaetoceros curvisetus, Ch. debilis in summer, Eucampia zodiacus in autumn, E. zodiacus, Thalassiosira curviseriata in winter and Skeletonema costatum -ls (like species), Leptocylindrus danicus in spring. According to principal component analysis, the phytoplankton community of the CWoCSS was characterized by the mixing rate between the freshwater inflow from Somjin River and the seawater of the South Sea, Korea.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Korean Journal of Environmental Biology
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• v.38 no.2
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• pp.254-270
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• 2020

We conducted a seasonal field survey to analyze the distribution patterns of a phytoplankton community and biological oceanographic characteristics in the Geum river estuary in 2018. The results showed that the phytoplankton community consisted of 58 genera and 116 species, showing a relatively simple distribution. It was controlled by diatoms at 70.2%, a low number of species in winter and spring, and a high number in summer and autumn. The phytoplankton cell density ranged from 10.0 to 2,904.0 cells mL^-1, with an average layer of 577.2 cells mL^-1, which was low in autumn and high in winter. The seasonal succession of phytoplankton dominant species was mainly centric diatoms from winter to summer, including Thalassiosira nordenskioeldii, Cerataulina bergonii, and Skeletonema costatum-ls in winter, S. costatum-ls and C. bergonii in spring, and Eucampia zodiacus and Th. nordenskioeldii in summer. However, the autumn species depended upon the regions, with the inner bay dominated by the centric diatom, Aulacoseira cf. granulata, the mixed areas by S. costatum-ls, and the open sea by the dinoflagellate, Lingulodinium polyedra. According to principal component analysis (PCA), the phytoplankton community was greatly affected by the inflow and expansion of freshwater, including high nutrients, which are introduced annually through the rivermouth weir in Geum river estuary. However, the estuary, which is strongly affected by annual freshwater, was limited to areas near Geumran Island, which is adjacent to the river-mouth weir.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.2
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• pp.95-111
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• 2012

Relationship between plankton assemblage and environmental factors in a semi-closed Chunsu Bay was examined. Temporal changes in phytoplankton assemblage was rather drastic than those found in most Korean coastal area in the Yellow Sea primarily due to the seawater temperature (T) and nutrient input from the dikes nearby. Freshwater discharge seemed to cause winter time increase of Diatoms (February) and summer time increase of Dinoflagellates at surface (July to August). Structural change in cell size with time was also found in Diatom. Zooplankton community structure was also changed with season probably due to the food concentration, seawater temperature and salinity (S). From principal component analysis (PCA) of zooplankton distribution, it was postulated that seasonal environmental changes such as T and S could explain about 32% of variability in zooplankton distribution along with phytoplankton cell numbers, while freshwater discharge could explain about 17%. Comparing with past data of 1985-1986, 1991-1992, the distributional patterns and percent composition of major species, Acartia hongi, Paracalanus parvus sensu lato and Centropages abdominalis, were similar. However, the abundances have been increased more than three times. The composition of other taxa than copepods showed significant changes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.9
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• pp.22-30
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• 2011

Recently, the rising demand for intelligent video surveillance system leads to high-performance face recognition systems. The solution for low-resolution images acquired by a long-distance camera is required to overcome the distance limits of the existing face recognition systems. For that reason, this paper proposes a hardware design of an image resolution enhancement algorithm for real-time intelligent video surveillance systems. The algorithm is synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images, called training set. When we checked the performance of the algorithm at 32bit RISC micro-processor, the entire operation took about 25 sec, which is inappropriate for real-time target applications. Based on the result, we implemented the hardware module and verified it using Xilinx Virtex-4 and ARM9-based embedded processor(S3C2440A). The designed hardware can complete the whole operation within 33 msec, so it can deal with 30 frames per second. We expect that the proposed hardware could be one of the solutions not only for real-time processing at the embedded environment, but also for an easy integration with existing face recognition system.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.527-536
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• 2017

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

• 한국해양학회지
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• v.23 no.3
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• pp.130-145
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• 1988

Spatial distribution and temporal variations of phytoplankton population were investigated in Ch$\check{o}$nsu Bay, the Korean western coast. Diurnal fluctuations of phytoplankton standing crop are associated with semidiurnal tidal cycle, as high concentration at low tide and low at high tide. In monthly variations of phytopolankton standing crop, the 1st peak occurrs in March and the 2nd one in August. The study area could be divided into two parts, outer bay and inner bay according to the physical and biological factors such as water temperature and salinity, and phytoplankton distribution patterns. The northern waters of the bay, however, may be affected by irregular fresh water influx through the lock of the dike. Because of the hydrographical differences among the surveyed stations, phytoplankton species succession patterns of each station have some differences. On the whole in this study area, Paralia sulcata and Skeletonema costatum are dominant species all the year round. However, except June, Paralia sulcata, a tychopelagic diatom is not dominant species at Station 6 (northern end of the bay). This seems to be caused by the fact that the waters of northern part of the bay is less turbulent than that of the outer bay. The result of principal component analysis (PCA) indicates that Ch$\check{o}$nsu Bay is normal coastal ecosystem where the environmental conditions are cycled in a year, and water temperature and nitrogenous nutrients such as nitrate, nitrite and ammonia are major factors to influence the annual cycle of environmental conditions.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.4
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• pp.371-376
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• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.