• Journal of Navigation and Port Research
• /
• v.40 no.3
• /
• pp.113-120
• /
• 2016

If there are containers stacked upon the container to be fetched out of a container yard to vessel, rehandling which moves those containers to other places temporarily is needed. In order to avoid such rehandling, remarshalling which rearranges containers should be done before the vessel arrives. The remarshalling plan is commonly generated within a bay. It happens, however, that the generation of the intra-bay remarshalling plan within the permitted time is not possible because of bad stacking conditions. This paper presents the remarshalling algorithm which uses the empty slots of the neighboring bay as a temporary storage space. Simulation experiments have shown that the presented algorithm can generate the remarshalling plan within the permitted time under any staking conditions.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.1
• /
• pp.110-114
• /
• 2010

We describe a new method for text categorization using TextRank algorithm. Text categorization is a problem that over one pre-defined categories are assigned to a text document. TextRank algorithm is a graph-based ranking algorithm. If we consider that each word is a vertex, and co-occurrence of two adjacent words is a edge, we can get a graph from a document. After that, we find important words using TextRank algorithm from the graph and make feature which are pairs of words which are each important word and a word adjacent to the important word. We use classifiers: SVM, Na$\ddot{i}$ve Bayesian classifier, Maximum Entropy Model, and k-NN classifier. We use non-cross-posted version of 20 Newsgroups data set. In consequence, we had an improved performance in whole classifiers, and the result tells that is a possibility of TextRank algorithm in text categorization.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.113-118
• /
• 2011

In this paper, we develop a speaker gender classification technique using collaborative sensor fusion for use in a wireless sensor network. The distributed sensor nodes remove the unwanted input data using the BER(Band Energy Ration) based voice activity detection, process only the relevant data, and transmit the hard labeled decisions to the fusion center where a global decision fusion is carried out. This takes advantages of power consumption and network resource management. The Bayesian sensor fusion and the global weighting decision fusion methods are proposed to achieve the gender classification. As the number of the sensor nodes varies, the Bayesian sensor fusion yields the best classification accuracy using the optimal operating points of the ROC(Receiver Operating Characteristic) curves_ For the weights used in the global decision fusion, the BER and MCL(Mutual Confidence Level) are employed to effectively combined at the fusion center. The simulation results show that as the number of the sensor nodes increases, the classification accuracy was even more improved in the low SNR(Signal to Noise Ration) condition.

• Journal of KIISE:Software and Applications
• /
• v.32 no.7
• /
• pp.674-682
• /
• 2005

This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.2
• /
• pp.115-125
• /
• 2003

As Web services are generally open for external uses and not filtered by Firewall, these result in attacker's target. Web attacks which exploit vulnerable web-applications and malicious users' requests cause economical and social problems. In this paper, we are modelling general web service usages based on user-web-session and detect anomal usages with Bayesian estimation method. Finally we propose SAD(Session Anomaly Detection) for detection unknown web attacks. To evaluate SAD, we made an experiment on attack simulation with web vulnerability scanner, whisker. The results show that the detection rate of SAD is over 90%, which is influenced by several features such as size of window or training set, detection filter method and web topology.

• Journal of the Korean Geotechnical Society
• /
• v.38 no.10
• /
• pp.17-29
• /
• 2022

Recently, the interest in integrated underground geospatial information mapping (IUGIM) to ensure the safety of underground spaces and facilities has been increasing. Because IUGIM is used in the fields of underground space development and underground safety management, the up-to-dateness and accuracy of information are critical. In this study, IUGIM and field data were classified, and the accuracy of IUGIM was improved by spatial interpolation. A spatial interpolation technique was used to process borehole data in IUGIM, and a quantitative evaluation was performed with mean absolute error and root mean square error through the cross-validation of seven interpolation results according to the technique and model. From the cross-validation results, accuracy decreased in the order of nonuniform rational B-spline, Kriging, and inverse distance weighting. In the case of Kriging, the accuracy difference according to the variogram model was insignificant, and Kriging using the spherical variogram exhibited the best accuracy.

• Journal of muscle and joint health
• /
• v.29 no.2
• /
• pp.100-112
• /
• 2022

Purpose: This study is a qualitative analysis of Korean-American (K-A) women's cancer prevention in the US. Methods: Qualitative research was conducted. Fifteen K-A women in four states were interviewed. Content theme analysis was used to analyze verbatim transcriptions of interviews. Results: Participants experienced difficulties in utilizing cancer screening programs. Factors include unfamiliarity with the US health care system, high health care costs or lack of health insurance, language barriers, and irregular and sporadic cancer screening participation. Participants also actively pursued non-institutional approaches to cancer prevention. They engaged in word-of-mouth informational exchanges in K-A communities, sought cancer screening in hospitals in Korea, conducted internet searches, autonomously decided on their health issues, and adopted healthy practices including better diets, physical exercise, and spiritual practices. Conclusion: It is necessary to implement measures to increase K-A women's utilization of the US cancer screening services and to encourage their active engagement in hands-on cancer prevention practices. K-A women should be empowered through increased familiarity with US cancer screening services and through the establishment of improved K-A community social services.

• The Korean Journal of Financial Management
• /
• v.18 no.1
• /
• pp.23-41
• /
• 2001

본 연구에서는 여러 계량 모형을 이용하여 계산한 헤지 비율의 성과를 비교하였다. 특히 헤지 비율을 추정하기 위하여 분수 공적분 오차 수정 모형을 이용하였다. KOSPI200 현물과 선물 지수를 이용하여 검증한 결과 현물, 선물 지수는 1차 적분된 시계열이며 베이시스는 분수 적분된 시계열이었다. 따라서 현물과 선물 지수는 분수 공적분된 시계열이었다. 최소 분산 헤지 비율을 최적 헤지 비율로 하여 성과를 측정한 결과 다음과 같은 결과를 얻었다. 헤지 성과는 GARCH 항이 있는 모형이 없는 모형에 비해 크게 나타나며 각 모형에서 고려하고 있는 정보 집합의 크기가 큰 순서인 FIEC, EC, VAR, OLS 순으로 헤지 성과는 크게 나타나고 있다. 그러나 OLS 방법에 의한 헤지에 의해서도 수익률 변동의 많은 부분이 사라져, 다른 모형들은 OLS 모형과 비교하여 추가적인 분산 감소 효과는 크지 않았다.

• Proceedings of the KSME Conference
• /
• 2007.05b
• /
• pp.3122-3127
• /
• 2007

Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and monitor the water pollution. The monitoring system, however, could determine whether the examined water was safe or not. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is given to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.89-90
• /
• 2008

AWGN(Addictive white gaussian noise)에 의해 영상은 자주 훼손되곤 한다. 최근 이를 복원하기위해 웨이블릿(Wavelet) 영역에서의 베이시안(Bayesian) 추정법이 연구되고 있다. 웨이블릿 변환된 영상 신호의 밀도 함수(pdf)는 표족한 첨두와 긴 꼬리(long-tail)를 갖는 경망이 있다. 이러한 사전 밀도 함수(a priori probability density function)를 상황에 적합하게 추정한다면 좋은 성능의 복원 결과를 얻을 수 있다. 빈번이 제안되는 릴도 함수로 가우시안(Gaussian) 분포 참수와 라플라스(Laplace) 분포 함수가 있다. 이들 각각의 모델은 훌륭히 변환 계수들을 모델링하며 나름대로의 장점을 나타낸다. 본 연구에서는 가우시안 분포와 라플라스(Laplace) 분포의 혼합 분포 모델을 밀도 함수로 제안하여, 이 들의 장점을 종합하였다. 이를 MAP(Maximum a Posteriori) 추정 방법에 적용하여 잡음을 제거 하였다. 그 결과 기존의 알고리즘에 비해 시각적인 면(Visual aspect), 수치적인 면(PSNR), 그리고 연산량(Complexity) 측면에서 망상된 결과를 얻었다.