• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.123-131
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• 2020

The text ranking algorithm is a representative method for keyword extraction, and its importance is emphasized highly. In this paper, we compare the performance of recent research and experiments with TF-IDF, SMART, INQUERY and CCA algorithms, which are used in text ranking algorithm.. After explaining each algorithm, we compare the performance of each algorithm based on the data collected from news and Twitter. Experimental results show that all of four algorithms can extract specific words from news data equally. However, in the case of Twitter, CCA has the best performance to extract specific words, and INQUERY shows the worst performance. We also analyze the accuracy of the algorithm through six comparison metrics. The experimental results present that CCA shows the best accuracy in the news data. In case of Twitter, TF-IDF and CCA show similar performance and demonstrate good performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.164-167
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• 2012

The technology which is used for segregating voices signals from exhaust noise signals of a car is very practical one to realize the interfaces between men and machines using voices. The voice signals contaminated by exhaust noise signal of a car was separated by canonical correlation ananysis(CCA) in an environment which does not guarantee the independence between signals and have prior informations. Rearrangement for the input signals is important in CCA. CCA was studied and segragation between source signals were performed by CCA through rearrangements of each of signals. It is possible to apply the technique to various signals since it is also possible to use CCA to the signals which are not independent.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.303-310
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• 2018

One of most shocking headlines is a serious chain collision accident (CCA). The development of CCA has a temporal and spatial locality, and the information of the CCA is time-critical. Due to these characteristics of CCA, traffic accident information should be rapidly propagated to drivers in order to reduce chain collisions, right after the first accident occurs. Inter-vehicle communication (IVC) based on ad-hoc communication is one of promising alternatives for locally urgent information propagation. Despite this potential of IVC, research for the effects of IVC on the reduction of CCA has not been reported so far. Therefore, this study develops the parallel platform of microscopic vehicle and IVC communication simulators and then analyses the effects of IVC on the reduction of the second collision related to a series of vehicles. To demonstrate the potential of the IVC-based propagation of urgent traffic accident information for the reduction of CCA, the reduction of approaching-vehicle speed, the propagation speed of accident information, and then the reduction of CCA were analysed, respectively, according to scenarios of combination of market rates and traffic volumes. The analysis results showed that CCA can be effectively reduced to 40~60% and 80~82% at the penetration rates of 10% and 50%, respectively.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1139-1149
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• 2014

Multivariate volatility is summarized through canonical correlation analysis (CCA). Along with the standard CCA, non-negative and sparse canonical correlation analysis (NSCCA) is introduced to make sure that volatility coefficients are non-negative and the number of coefficients in the volatility CCA is as small as possible. Various multivariate financial time series are analyzed to illustrate the main contribution of the paper.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2006.04a
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• pp.234-237
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• 2006

방부 목재는 일반 목재에 비해 사용 수명이 20-40년 정도 길기 때문에 사용이 점차 증가하고 있다 국내에서 가장 널리 사용되는 방부제는 chromate copper arsenate(CCA)이다 CCA 성분인 구리, 크롬 및 비소는 생태계 및 인체에 대한 위해성 때문에 많은 나라에서 처리 목재의 사용을 금지하거나 제한하고 있는 실정인 반면에, 국내에서의 사용은 늘어만 가고 있는 실정이다. 이 연구에서는 방부 목재를 사용하여 지은 3년 된 통나무집 한 곳과 8년 된 통나무집 두 곳에 인접한 토양시료를 채취, 분석하여 방부 목재 사용으로 인한 토양 오염도를 평가하였다. 건물에 인접한 지점부터 수평 방향으로 25cm 간격으로 100cm까지 5개의 표토 시료를 채취하였다. 또한 배경 농도를 알아보기 위해서 건물에서 조금 떨어진 곳에서 토양시료를 2개 채취하였다. 토양시료는 입도, 전기전도도, pH, 유기물 함량 등의 물리 화학적 성질에 대해 분석하였으며, CCA성분은 microwave oven을 이용하여 추출한 후 분석하였다. 0cm에서 CCA성분은 배경 농도보다. 높게 나타났다. 용출양은 3년 된 통나무집의 경우 크롬(67.2mg/kg)>구리(20.3mg/kg)>비소(4.14mg/kg)의 순으로 측정되었으며, 8년 된 통나무집의 경우 크롬(36.6mg/kg)>구리(21.3mg/kg)>비소(1.93mg/kg)의 순으로 측정되었다. 구리를 제외하고 크롬과 비소의 경우 3년 된 통나무집에서 많이 용출되는 것을 알 수 있었다. 3년 된 통나무집의 구리와 크롬은 100cm농도가 배경농도보다. 높게 나타났으며, 비소의 경우는 100cm농도가 배경농도보다. 낮게 나타났다. 이는 구리와 크롬이 100cm이상으로 이동을 한다는 것을 알려주며, 8년 된 통나무집의 경우는 크롬과 비소가 100cm이상으로 이동한다는 것을 알 수 있었다. 이 연구를 통해 CCA로 처리된 방부목재에서는 CCA성분이 용출되는 것을 알 수 있었으며, 크롬과 비소의 경우는 초기에 많이 용출되고, 구리의 경우는 꾸준히 용출되는 것을 알 수 있었다. 3년 된 통나무집이 8년 된 통나무집보다. 용출양이 더 컸으며, 이는 CCA성분이 초기에 많이 용출된다는 것을 의미한다.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.279-284
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• 2017

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative time delay between two or more received signals for the direct signal must be determined. Although the GCC (Generalized Cross-Correlation) method is the most popular technique, an approach based on CCA (Canonical Correlation Analysis) was also proposed for the TDE (Time Delay Estimation). In this paper, we propose a new adaptive algorithm based on CCA in order to utilized the sparsity in the eigenvector of CCA based time delay estimator. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue with log-sum regularization in order to utilize the sparsity in the eigenvector. We have performed simulations for several SNR(signal to noise ratio)s, showing that the new CCA based algorithm can estimate the time delays more accurately than the conventional CCA and GCC based TDE algorithms.

• The KIPS Transactions:PartC
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• v.16C no.3
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• pp.393-406
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• 2009

This paper introduces two functions added to the current version of ns-2 simulator for better accuracy of IEEE 802.15.4 network simulations. The first one is to automatically place hidden nodes over the ring topology in which the coordinator is centered, when the number of hidden nodes and total number of nodes is given. Collisions of signals can be distinguished into the trace file according to the ways of participation of hidden nodes. The second one is the CCA deferment algorithm described in IEEE 802.15.4-2006 standard which is not implemented in the current version of ns-2. Owing to these additional functions, we can carry out the precise analysis of the performance effects of hidden nodes and CCA deferment algorithm on 802.15.4 networks. Simulation results present at least 66% of performance degradation in throughput and drastic increase of collision probability up to 90% from 65% by just a single hidden node. Besides, 2006 standard for CCA deferment algorithm gives 19% lower collision probability and 38% higher performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.139-147
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• 2015

Steady state visual evoked potential (SSVEP) has been actively studied because of its short training time, relatively higher signal-to-noise ratio, and higher information transfer rate. There are two popular analysis methods for SSVEP signals: power spectral density analysis (PSDA) and canonical correlation analysis (CCA). However, the PSDA is known to be vulnerable to noise due to the use of a single channel. Although conventional CCA is more accurate than PSDA, it may not be appropriate for the real-time SSVEP-based BCI system when it has short time window length because it uses sinusoidal signals as references. Therefore, the two methods are not efficient for the real-time BCI system that requires a short TW and a high recognition accuracy. To overcome this limitation of the conventional methods, this paper proposes a frequency recognition method with a combination of CCA and PSDA using the difference between powers of canonical variables obtained from the results of CCA. Experimental results show that the performance of the combination of CCA and PSDA is better than that of CCA for the case of a short TW.

• Analytical Science and Technology
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• v.21 no.1
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• pp.1-8
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• 2008

This study was conducted to investigate the characteristics of the leaching of metal components from CCA-treated wood during outdoor exposure. CCA-treated wood specimens were placed horizontally or buried vertically into the soil, and then exposed to the natural environment for a year. Wood samples were collected from the side of the horizontal wood specimens using a drill and saw dust samples were collected at the end of the exposure. Soil samples were also obtained around the wood specimens and at different depths of the posts. Wood and soil samples were analyzed for metals using an atomic absorption spectrometer. Monthly metal concentrations varied greatly and more metals were released when wood specimens were exposed vertically than horizontally. Arsenic was released from the wood by 80 % of the intial content. In addition, more leaching was observed from the zone below the ground than above the ground, and soil around the posts was contaminated with metals released from CCA-treated wood.