• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.41-53
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• 2008

Unprecedented weather phenomena are occurring because of climate change: extreme heavy rains, heat waves, and severe rain storms after the rainy season. Recently, the frequency of these abnormal phenomena has increased. However, regular pattern or cycles cannot be found. Analysis of annual data or annual average data, which has been established a research method of climate change, should be applied to find frequency and tendencies of extreme climate events. In this paper, extreme indicators of precipitation and temperature marked by objectivity and consistency were established to analyze data collected by 66 observatories throughout Korea operated by the Meteorological Administration. To assess the statistical significance of the data, linear regression and Kendall-Tau method were applied for statistical diagnosis. The indicators were analyzed to find tendencies. The analysis revealed that an increase of precipitation along with a decrease of the number of rainy days. A seasonal trend was also found: precipitation rate and the heavy rainfall threshold increased to a greater extent in the summer(June-August) than in the winter (September-November). In the meanwhile, a tendency of temperature increase was more prominent in the winter (December-February) than in the summer (June-August). In general, this phenomenon was more widespread in inland areas than in coastal areas. Furthermore, the number of winter frost days diminished throughout Korea. As was mentioned in the literature, the progression of climate change has influenced the increase of temperature in the winter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.550-559
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• 2018

The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.689-698
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• 2006

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.47-53
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• 2002

In this paper, we proposed a content-based video search method using the eigen value of key frame and intensity component. We divided the video stream into shot units to extract key frame representing each shot, and get the intensity distribution of the shot from the database generated by using ECA(Eigen Component Analysis). The generated codebook, their index value for each key frame, and the intensity values were used for database. The query image is utilized to find video stream that has the most similar frame by using the euclidean distance measure among the codewords in the codebook. The experimental results showed that the proposed algorithm is superior to any other methols in the search outcome since it makes use of eigen value and intensity elements, and reduces the processing time etc.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.163-168
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• 2009

Most image segmentation methods are to represent observed feature vectors at each pixel, which are assumed as appropriated probability models. These models can be used by statistical estimating or likelihood clustering algorithms of feature vectors. EM algorithms have some calculation problems of maximum likelihood for unknown parameters from incomplete data and maximum value in post probability distribution. First, the performance is dependent upon starting positions and likelihood functions are converged on local maximum values. To solve these problems, we mixed the Gausian function and histogram at all the level values at the image, which are proposed most suitable image segmentation methods. This proposed algoritms are confirmed to classify most edges clearly and variously, which are implemented to MFC programs.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.287-293
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• 2015

This study proposed signal processing techniques and neural network with weighted fuzzy membership functions(NEWFM) to detect epileptic seizure from EEG signals. This study used wavelet transform(WT), sequential increment method, and phase space reconstruction(PSR) as signal processing techniques. In the first step of signal processing techniques, wavelet coefficients were extracted from EEG signals using the WT. In the second step, sequential increment method was used to extract peaks from the wavelet coefficients. In the third step, 3D diagram was produced from the extracted peaks using the PSR. The Euclidean distances and statistical methods were used to extract 16 features used as inputs for NEWFM. The proposed methodology shows that accuracy, specificity, and sensitivity are 97.5%, 100%, 95% with 16 features, respectively.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.93-102
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• 2003

A slant correction method for handwritten Korean strings based on analysis of stroke distribution, which effectively reflects structural properties of Korean characters, is presented in this paper. The method aims to deal with typical problems which have been frequently observed in slant correction of handwritten Korean strings with conventional approaches developed for English/European languages. Extracted strokes from a line of text image are classified into two clusters by applying the K-means clustering. Gaussian modeling is applied to each of the clusters and the slant angle is estimated from the model which represents the vertical strokes. Experimental results support the effectiveness of the proposed method. For the performance comparison 1,300 handwritten address string images were used, and the results show that the proposed method has more superior performance than other conventional approaches.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.873-900
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• 2017

본 연구는 사회연결망 분석이론을 통해 원자력 과학기술에 대한 사회과학 연구의 경향적 특징을 파악하고, 동 분야의 주요 연구주제와 하부 연구분야를 도출하기 위해 수행되었다. 연구대상은 1957년부터 2016년까지 국내 학술지에 게재된 원자력 관련 사회과학 분야 연구논문 605건으로, 저자가 제시한 키워드 간 관계망 형성을 통해 네트워크 분석을 수행하였다. 분석결과, 첫째 국내에서 수행된 원자력 관련 사회과학 연구의 기술통계적 특징을 확인하였다. 원자력 사회과학 연구는 1957년부터 시작되어 꾸준히 수행되어졌는데, 2011년을 기점으로 논문발표가 급격히 증가했다. 주로 법학, 행정학, 정책학, 정치학의 연구가 대학 내 연구자를 중심으로 수행되어 왔다. 원자력 관련 기술개발이 주로 정부 출연연구기관에서 수행된다는 점을 고려 했을 때, 향후 사회과학 분야에 있어 대학과 출연기관 간의 역할분담이 필요하다. 둘째, 후쿠시마 원전사고가 발생한 2011년을 기점으로 사회과학의 원자력에 관한 연구가 양적, 질적으로 본격적으로 활성화 되었다. 원자력 관련 사회과학 지식 네트워크는 2011년 이전에 비해 규모면에서 큰 차이를 보였다. 또한, 네트워크 중심성 분석결과, 후쿠시마 사고 이전 사회과학 연구자들의 연구경향은 핵비확산, 과학기술 정책, 사회수용성이었다면, 후쿠시마 이후에는 원전해체, 손해배상, 에너지믹스, 탈핵운동 등과 같은 다양한 원자력 현안으로 확대되었다. 셋째, 하부 연구분야 도출을 통해 특정 연구주제별 쏠림현상을 확인했다. 하부 네트워크 분석 결과, 제시된 9개의 하부 연구분야는 네트워크 속성 값에서 차이를 보였다. 특히, 법학 관련 연구주제가 가장 높은 밀도를 갖는 반면 지속가능 발전과 에너지 믹스 관련 연구주제의 밀도가 가장 낮게 나타났다. 본 연구는 원자력에 관련된 학자의 인식을 연구경향 분석을 통해 파악한다는 점에서 의의가 있으며, 이는 추후 원자력 관련 정책연구자 혹은 정책결정자에게 유용한 기초자료를 제공할 것이라 기대된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.494-499
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• 2012

In this paper, a new method for detecting various objects that can be risks to safety navigation in sea environment is proposed. By analysing Infrared(IR) images obtained from various sea environments, we could find out that object regions include both horizontal and vertical direction edges while background regions of sea surface mainly include vertical direction edges. Therefore, we present an approach to detecting object regions considering horizontal and vertical edges. To this end, in the first step, image enhancement is performed by suppressing noises such as sea glint and complex clutters using a statistical filter. In the second step, a horizontal edge map and a vertical edge map are generated by 1-D Discrete Cosine Transform technique. Then, a combined map integrating the horizontal and the vertical edge maps is generated. In the third step, candidate object regions are detected by a adaptive thresholding method. Finally, exact object regions are extracted by eliminating background and clutter regions based on morphological operation.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.56-62
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• 2001

In this paper, we adopt a missing data theory to speech recognition. It can be used in order to maintain high performance of speech recognizer when the missing data occurs. In general, hidden Markov model (HMM) is used as a stochastic classifier for speech recognition task. Acoustic events are represented by continuous probability density function in continuous density HMM(CDHMM). The missing data theory has an advantage that can be easily applicable to this CDHMM. A marginalization method is used for processing missing data because it has small complexity and is easy to apply to automatic speech recognition (ASR). Also, a spectral subtraction is used for detecting missing data. If the difference between the energy of speech and that of background noise is below given threshold value, we determine that missing has occurred. We propose a new method that examines the reliability of detected missing data using voicing probability. The voicing probability is used to find voiced frames. It is used to process the missing data in voiced region that has more redundant information than consonants. The experimental results showed that our method improves performance than baseline system that uses spectral subtraction method only. In 452 words isolated word recognition experiment, the proposed method using the voicing probability reduced the average word error rate by 12％ in a typical noise situation.