• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.120-123
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• 2013

본 논문에서는 키넥트 센서의 RGB영상을 이용하여 얼굴을 검출하고 검출된 영역의 깊이정보를 템플릿으로 사용하여 다수개의 얼굴을 추적하는 방법을 제안한다. 이 논문은 [1]의 단일 얼굴 추적방법을 다수의 얼굴을 추적하도록 확장한 것이다. 다수의 얼굴추적을 실시간으로 처리하기 위하여 영상을 down sampling 하여 사용한다. 얼굴 검출은 기본적으로 기존의 Adaboost 방법을 사용하나, 피부색만을 이용, 탐색영역을 최대한 축소하여 수행 시간 및 오검출율을 줄인다. 얼굴추적은 깊이정보를 템플릿으로 하며, 깊이값에 따라 크기, 탐색영역을 조정하고, 또한 일정 프레임마다 얼굴을 검출하며 겹침, 새로 나타남, 영상 밖으로 사라짐 등의 얼굴추적 시 발생하는 문제를 해결한다.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.75-82
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• 2013

The simultaneous analysis and monitoring of aflatoxin $B_1$, $G_1$, $B_2$, $G_2$ and ochratoxin A in foods were carried out by HPLC with fluorescence detection. The samples were extracted with methanol/water mixture. The extract was centrifuged, diluted with phosphate buffer saline (PBS), filtered, and applied to an immunoaffinity column containing antibodies specific to both aflatoxins and ochratoxin A. After washing the column with PBS and water, the toxins were eluted from the column with methanol, and quantified by HPLC, with a run time of approximately 30 min. The recoveries for aflatoxin $B_1$, $G_1$, $B_2$, $G_2$ and ochratoxin A in foods were 78.4~101.5%, 73.3~102.1%, 81.7~106.7%, 67.0~104.6% and 78.7~120.8%, respectively. The limits of detection of aflatoxins and ochratoxin A ranged from 0.05 to $0.18{\mu}g/kg$. According to monitoring result with the established method, aflatoxin $B_1$ and ochratoxin A were found in 13 of 151 domestic commercial foods. The contamination levels were $0.32{\sim}1.80{\mu}g/kg$ for aflatoxin $B_1$ and $0.97{\mu}g/kg$ for ochratoxin A. Therefore, this study showed all commercial foods monitored were safe under the Korean standards for aflatoxins and ochratoxin A.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.164-170
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• 2015

This paper deals with the adaptation of classification model in the binary mask approach to suppress noise in the noisy environment. The binary mask estimation approach is known to improve speech intelligibility of noisy speech. However, the same type of noisy data for the test data should be included in the training data for building the classification model of binary mask estimation. The eigenvoice adaptation is applied to the noise-independent classification model and the adapted model is used as noise-dependent model. The results are reported in Hit rates and False alarm rates. The experimental results confirmed that the accuracy of classification is improved as the number of adaptation sentences increases.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.405-413
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• 2012

Low misclassification performance is significant with high classification accuracy for a reliable diagnosis of ECG signals, and diagnosing abnormal state as normal state can especially raises a deadly problem to a person in ECG test. In this paper, we propose detection and classification method of abnormal rhythm by rule-based rhythm classification reflecting clinical criteria for disease. Rule-based classification classifies rhythm types using rule-base for feature of rhythm section, and rule-base deduces decision results corresponding to professional materials of clinical and internal fields. Experimental results for the MIT-BIH arrhythmia database show that the applicability of proposed method is confirmed to classify rhythm types for normal sinus, paced, and various abnormal rhythms, especially without misclassification in detection aspect of abnormal rhythm.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.545-551
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• 2005

The main objective of fraud detection is to minimize costs or losses that are incurred due to fraudulent transactions. Because of the problem's nature such as highly skewed, overlapping class distribution and non-uniform misclassification costs, it is, however, practically difficult to generate a classifier that is near-optimal in terms of classification costs at a desired operating range of rejection rates. This paper defines a performance measure that reflects classifier's costs at a specific operating range and offers a cost-sensitive learning approach that enables us to train classifiers suitable for real-world credit card fraud detection by directly optimizing the performance measure with evolutionary programming. The experimental results demonstrate that the proposed approach provides an effective way of training cost-sensitive classifiers for successful fraud detection, compared to other training methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.472-477
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• 2017

Oil spills cause a lot of damage to the environment. Oil destroys the water environment and ecosystem in a very short period of time once they are contaminated by it, it takes a lot of time to recover from the contamination and the cleaning process is very difficult. Therefore, oil detectors are greatly needed as they can monitor any oil spills over the sea, rivers, and lakes. There are two kinds of technology available for detecting oil, viz. the contact and non-contact types. The former is based on the use of the conductivity, capacitance and microwaves, while the latter employs infrared, UV, laser, optic and radar technologies. As there are also various hurdles in the measuring of oil on water, such as the presence of waves, refraction of light, temperature and saltiness, it is imperative to select the right oil detector which is appropriate for the specific environment. In this study, a contact type oil detector is developed, which can be used in oil related industries, such as refineries, petrochemical companies, and power generation stations. The detector is made up of the sensor module, which floats on the water, and the controller which processes the signal coming from the sensor module and displays it. It is designed in such a way that the existence of oil is detected through the sensor and the change in the permittivity is observed to determine the volume and type of spilled oil.

• Proceedings of the KAIS Fall Conference
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• 2005.05a
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• pp.214-216
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• 2005

본 논문에서는 가변형식메시지체계에서 COMSEC 프레임동기 정보의 전송영향을 분석하였다. 실험결과 비트오류율 $10^{-1}\~10^{-5}$ 환경에서 COMSEC 프레임 동기 정보의 강인한 특성을 고찰하였으며 암호통신을 위해 사용되는 프레임 동기정보가 동기검출 및 오검출 측면에서 영향정도를 분석하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.119-128
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• 2014

In this paper, a fast human detection method using adaptive UV-histogram and template matching is proposed. The proposed method improves the detection rate in the scene of complex environment. The method firstly generates U-histogram to extract human candidates and adaptively generates V-histogram for each labled U-histogram, thus it could extract humans correctly, which was impossible in the previous method. The method tries to match the human candidates with the adaptively sized omega shape template to the focal length and distance in order to improve the detection accuracy. It also detects false positives by rematching the template with accumulated foreground images and hence is robust to the occlusion. Experimental results showed that the proposed method has superior performance to the Bae's method in the complex environment with about 15% improvement in precision and 80% in recall and has 20 times faster processing time than Xia's method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.586-599
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• 2012

This paper proposes a fast face detection and tracking method which uses depth images as well as RGB images. It consists of the face detection procedure and the face tracking procedure. The face detection method basically uses an existing method, Adaboost, but it reduces the size of the search area by using the depth image. The proposed face tracking method uses a template matching technique and incorporates an early-termination scheme to reduce the execution time further. The results from implementing and experimenting the proposed methods showed that the proposed face detection method takes only about 39% of the execution time of the existing method. The proposed tracking method takes only 2.48ms per frame with $640{\times}480$ resolution. For the exactness, the proposed detection method showed a little lower in detection ratio but in the error ratio, which is for the cases when a detected one as a face is not really a face, the proposed method showed only about 38% of that of the previous method. The proposed face tracking method turned out to have a trade-off relationship between the execution time and the exactness. In all the cases except a special one, the tracking error ratio is as low as about 1%. Therefore, we expect the proposed face detection and tracking methods can be used individually or in combined for many applications that need fast execution and exact detection or tracking.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1825-1838
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• 2012

This paper proposes a fast face detection and tracking method which uses depth images as well as RGB images. It consists of the face detection procedure and the face tracking procedure. The face detection method basically uses an existing method, Adaboost, but it reduces the size of the search area by using the depth information and skin color. The proposed face tracking method uses a template matching technique and incorporates an early-termination scheme to reduce the execution time further. The results from implementing and experimenting the proposed methods showed that the proposed face detection method takes only about 39% of the execution time of the existing method. The proposed tracking method takes only 2.48ms per frame. For the exactness, the proposed detection method and previous method showed a same detection ratio but in the error ratio, which is about 0.66%, the proposed method showed considerably improved performance. In all the cases except a special one, the tracking error ratio is as low as about 1%. Therefore, we expect the proposed face detection and tracking methods can be used individually or in combined for many applications that need fast execution and exact detection or tracking.