• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.687-696
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• 2019

Conventional feature of seismic classification focuses on strong seismic classification, while it is not suitable for classifying micro-seismic waves. We propose a feature extraction method based on histogram and Principal Component Analysis (PCA) in frequency-time space suitable for classifying seismic waves including strong, micro, and artificial seismic waves, as well as noise classification. The proposed method essentially employs histogram and PCA based features by concatenating the frequency and time information for binary classification which consist strong-micro-artificial/noise and micro/noise and micro/artificial seismic waves. Based on the recent earthquake data from 2017 to 2018, effectiveness of the proposed feature extraction method is demonstrated by comparing it with existing methods.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.65-84
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• 2023

The method of Lagrange multipliers, one of the most fundamental algorithms for solving equality constrained optimization problems, has been widely used in basic mathematics for artificial intelligence (AI), linear algebra, optimization theory, and control theory. This method is an important tool that connects calculus and linear algebra. It is actively used in artificial intelligence algorithms including principal component analysis (PCA). Therefore, it is desired that instructors motivate students who first encounter this method in college calculus. In this paper, we provide an integrated perspective for instructors to teach the method of Lagrange multipliers effectively. First, we provide visualization materials and Python-based code, helping to understand the principle of this method. Second, we give a full explanation on the relation between Lagrange multiplier and eigenvalues of a matrix. Third, we give the proof of the first-order optimality condition, which is a fundamental of the method of Lagrange multipliers, and briefly introduce the generalized version of it in optimization. Finally, we give an example of PCA analysis on a real data. These materials can be utilized in class for teaching of the method of Lagrange multipliers.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.418-427
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• 2020

The measurement of meat freshness at meat markets is important for the health of consumers. Currently a variety of sensors have been studied for the measurement of the meat freshness. Therefore, the analysis of sensor data is needed for the reduction of measurement errors. In this paper, we analyze the freshness measurement data of ten sensors based on deep learning. The measured data are composed of beef, pork and chicken, whose reliability and noise-robustness are examined by a deep neural network. Further, to search for multiple sensors better than a torrymeter, PCA (principle component analysis) is carried. Then, we validated that the performance of the three sensors outperforms the torrymeter in the experiment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1114-1119
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• 2001

Previously, we have analyzed Vehicle interior noise by dBA based analysis. However, dBA based analysis can not describe the various sound phenomenon that consumer hear. Sound quality matrics can describe various sound phenomenon that dBA based analysis could not explain. In this paper, we will demonstrate the difference of between dBA based analysis and real sound feeling, and analyze sound examples by sound metrics and Principle Component Analysis. In this way we can analyze vehicle interior noise more effectively.

• Korean Journal of Agricultural Science
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• v.41 no.4
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• pp.309-314
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• 2014

A total of 67 watermelon accessions which include 37 accessions from Korean and 27 accessions from Turkish germplasm and 3 accessions of other related species from USA were investigated for morphological characteristics. The UPOV descriptor list for 56 characters (6 seedlings, 4 plants, 11 leaves, 5 flowers, 23 fruits and 7 seeds) was used in characterization. In addition, eight quantitative characters, hypocotyl length, cotyledon width, cotyledon length, fruit weight, fruit length, fruit width, thickness of outer layer of pericarp and soluble solid content were also measured. The 56 qualitatively scored characters were analyzed by principle coordinate analysis (PCoA) while the eight quantitative ones were subjected to principle component analysis (PCA). Morphological characterization result demonstrated that the accessions displayed high morphological diversity(how much percent?). A high level of phenotypic diversity was observed from the results of morphological characterization. However, plant growth habit and leaf blade flecking showed constant characters for all of the accessions. The Korean and Turkish watermelon genotypes are diverse groups and can be separated by both multivariate analysis of morphological characters although the grouping was more apparent in PCoS results.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.125-128
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• 2000

A recycling reactor system operated under sequential anoxic and oxic conditions for the swine wastewater has been developed, in which piggery slurry is fermentatively and aerobically treated and then part of the effluent recycled to the pigsty. This system significantly removes offensive smells (at both pigsty and treatment plant), BOD and other loads, and appears to be costeffective for the small-scale farms. The most dominant heterotrophs were Alcaligenes faecalis, Brevundimonas diminuta and Streptococcus sp. in order while lactic acid bacteria were dominantly observed in the anoxic tank. We propose a novel monitoring system for a recycling piggery slurry treatment system through neural networks. Here we tried to model treatment process for each tank(influent, fermentation, aeration, first sedimentation and fourth sedimentation tanks) in the system based on population densities of heterotrophic and lactic acid bacteria. Principle component analysis(PCA) was first applied to identify a relation between input(microbial densities and parameters for the treatment such as population densities of heterotrophic and lactic acid bacteria, suspended solids (SS), COD, $NH_3-N$, ortho-P, and total-P) and output, and then multilayer neural networks were employed to model the treatment process for each tank. PCA filtration of input data as microbial densities was found to facilitate the modeling procedure for the system monitoring even with a relatively lower number of input. Neural networks independently trained for each treatment tank and their subsequent combinatorial data analysis allowed a successful prediction of the treatment system for at least two days.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Bulletin of the Korean Chemical Society
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• v.31 no.11
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• pp.3151-3155
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• 2010

This study was performed to develop analytical methods to better understand the properties and reactivity of petroleum, which is a highly complex organic mixture, using high-resolution mass spectrometry and statistical analysis. Ten crude oil samples were analyzed using negative-mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). Clustering methods, including principle component analysis (PCA), hierarchical clustering analysis (HCA), and k-means clustering, were used to comparatively interpret the spectra. All the methods were consistent and showed that oxygen and sulfur-containing heteroatom species played important roles in clustering samples or peaks. The oxygen-containing samples had higher acidity than the other samples, and the clustering results were linked to properties of the crude oils. This study demonstrated that clustering methods provide a simple and effective way to interpret complex petroleomic data.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.384.1-384.1
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• 2016

We reported that amorphous silicon (a-Si) thin film provide sample plate exhibiting a multimodality to measure biomolecules by secondary ion mass spectrometry (SIMS) and laser desorption/ionization mass spectrometry (LDI-MS). Kim et al.1 reported that a-Si thin film were suitable to detect small molecules such as drugs and peptides by SIMS and LDI-MS. Recently, bacterial identification has been required in many fields such as food analysis, veterinary science, ecology, agriculture, and so on.2 Mass spectrometry is emerging for identifying and profiling microbiology samples from its advantageous characters of label-free and shot-time analysis. Five species of bacteria - S. aureus, G. glutamicum, B. kurstaki, B. sphaericus, and B. licheniformis - were sampled for MS analysis without lipid extraction in sample preparation steps. The samples were loaded onto the a-Si thin film with a thickness of 100 nm which did not only considered laser-beam penetration but also surface homogeneity. Mass spectra were recorded in both positive and negative ionization modes for more analytical information. High reproducibility and sensitivity of mass spectra were demonstrated in a mass range up to mass-to-charge ratio(m/z) 1200 by applying the a-Si thin film in mentioned above MS. Principle component analysis (PCA) - a popular statistical analysis widely used in data processing was employed to differentiate between five bacterial species. The PCA results verified that each bacterial species were readily distinguished and differentiated effectively from our MS approach. It shows a new opportunity to rapid bacterial profiling and identification in clinical microbiology. More details will be discussed in the presentation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.183-191
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• 2010

Unique and invariant biometric characteristics have been used for secure user authentication. Storing original biometric data is not acceptable due to privacy and security concerns of biometric technology. In order to enhance the security of the biometric data, the cancelable biometrics was introduced. Using revocable and non-invertible transformation, the cancelable biometrics can provide a way of more secure biometric authentication. In this paper, we present a new cancelable bits extraction method for the facial data. For the feature extraction, the Subpattern-based Principle Component Analysis (PCA) is adopted. The Subpattern-based PCA divides a whole image into a set of partitioned subpatterns and extracts principle components from each subpattern area. The feature extracted by using Subpattern-based PCA is discretized with a helper data based method. The elements of the obtained bits are evaluated and ordered according to a measure based on the fisher criterion. Finally, the most discriminative bits are chosen as the biometric bits string and used for authentication of each identity. Even if the generated bits string is compromised, new bits string can be generated simply by changing the helper data. Because, the helper data utilizes partial information of the feature, the proposed method does not reveal privacy sensitive biometric information of the user. For a security evaluation of the proposed method, a scenario in which the helper is compromised by an adversary is also considered.