• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.216-219
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• 2007

본 논문에서는 영상 인식을 위해 그레이 레벨로 획득된 영상을 이진화할 때 발생되어 패턴의 윤곽선을 울퉁불퉁하게 만드는 랜덤 노이즈를 제거하기 위한 방법으로 체인 코드 트리밍(chain code trimming) 을 제안한다. 제안된 방법은 패턴의 외부 윤곽선과 내부 윤곽선의 체인 코드 분석을 통해 랜텀 노이즈의 체인 코드를 제거, 교정함으로서 이루어진다. 실험에서는 트리밍을 사용하기전과, 단순 트리밍을 한 경우, 체인 코드 트리밍을 한 경우를 서로 바로, 분석한다. 실험 결과는 패턴에 첨부되었던 랜덤 노이즈가 모두 성공리에 제거된 것을 보인다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.227-236
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• 2003

This paper proposes a modified jointly-blue noise mask (MJBNM) method using the S-CIELAB color measure as digital color halftoning method. Based on an investigation of the relation between the pattern visibility and the chromatic error, of a blue noise pattern, a halftoning method is proposed that reduces the chromatic error, while preserving a high quality blue noise pattern. Accordingly, to reduce the chrominance error, the low-pass filtered error and S-CIELAB chrominance error are both considered during the mask generation procedure and calculated for single and combined patterns. Using the calculated low-pass filtered error, the patterns are then updated by either adding or removing dots from the multiple binary patterns. Finally, the pattern exhibiting the lower S-CIELAB chrominance error is selected. Experimental results demonstrated that the proposed algorithm can produce a visually pleasing half toned image with a lower chrominance error than the JBNM method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.15-22
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• 2017

With the advance of Internet Technology, various social network services are created and used by users. Especially, the use of smart devices makes that multimedia contents can be used and distributed on social network services. However, since the crime rate also is increased by users with illegal purposes, there are needs to protect contents and block illegal usage of contents with multimedia forensics. In this paper, we propose a multimedia forensic technique which is identifying the video source. First, the scheme to acquire the sensor pattern noise (SPN) using morphology filtering is presented, which comes from the imperfection of photon detector. Using this scheme, the SPN of reference videos from the reference device is estimated and the SPN of an unknown video is estimated. Then, the similarity between two SPNs is measured to identify whether the unknown video is acquired using the reference device. For the performance analysis of the proposed technique, 30 devices including DSLR camera, compact camera, camcorder, action cam and smart phone are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 96% accuracy in identification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.918-920
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• 2011

In this paper, a simple edge pattern classification method is proposed for detecting straight line segments in an image corrupted by impulse noise. Corrupted images have complicated edge patterns. To detect straight line from an complicated edge pattern, it is needed to simplify the entire edge. The proposed algorithm separates the entire edge into 4 directional partial edge patterns. Each line segment is separated from the partial edge image where several line segments are overlapped, and then the straight line is detected. The results of the experiments emphasize that the proposed algorithm is simple but accurate.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.05a
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• pp.413-418
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• 2002

베어링은 산업현장에서 널리 쓰여지는 중요 부품이다. 따라서 이의 결함에 따른 손실을 예방하기 위해서는 이상을 진단하고 검지하는 기법이 요구된다. 따라서 본 연구에서는 저속회전하므로 노이즈가 많이 포함되어 절상상태의 신호검출이 어려운 저속회전베어링의 외륜이상을 웨이브렛의 Denoising 기법을 적용하여 정량적으로 진단하고 패턴인식법 중의 하나인 KDI(Kullback Discrimination Information)를 적용하여 이상상태의 진단/검지능력을 시험해 보았다. 웨이브랫의 Denoising 기법은 노이즈 캔셀링(Noise canceling)이 능력이 뛰어났고, HDI기법은 저속회전베어링의 정상과 이상의 분류에 뛰어난 검지능력이 있음을 알 수 있었다.(중략)

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.690-693
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• 2006

As the silence of vehicle is more important, noise reduction of tire is more required. Noise of tire is divided into structure home noise and air borne noise. Tire tread has the property such as Hardness. Pattern Noise is caused by changing of tread hardness. This property has influence on the mechanisms which are Block Impact & Stick-slip sound. In the study, we found that the effect of Hardness is related to more Stick-Slip than Impact.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.241-244
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• 2002

본 논문은 다치(MVL : Multiple Valued Logic) 신경망의 BP 알고리즘을 이용하여 패턴 인식에 응용하는 방법을 제안한다. 패턴처리에 필요한 원 패턴에 대한 물체 농도의 특징을 추출하고, 물체 농도의 특징을 다치로 사상시킨다. 또한 다치 신경망을 이용하여 원 패턴을 학습을 시킨 다음, 노이즈 패턴을 제거하여 원 패턴에 근접한 패턴을 인식하게 되므로, 패턴에 필요한 시간 및 기억 공간을 최소화할 수 있다.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.491-498
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• 2017

This study is to determine the significant effect of white and pink noise on brain. The brain synchronization has been analyzed under the condition of non-noise, white nose and pink noise(male 10, female 10, mean age $23.3{\pm}2.14$). As a result of analysis, pink noise stimulus, alpha, low beta band, and high beta band were significantly decreased than non-noise and white noise. In addition, these brain response pattern significantly increased at frontal lobe and temporal lobe, and dominated on the right hemisphere. This result is considered to be useful of sound design in driving quality of human life on the basis of neuroscience.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.382-384
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• 2020

데이터 클러스터링은 데이터의 숨겨진 패턴을 찾아낸다. 시계열 데이터에서 시차가 존재하는 데이터를 클러스터링하는 것은 데이터의 미래 패턴을 찾아내기 위해서 사용한다. 데이터 클러스터링을 수행하기 위한 여러 가지 Metric이 존재하지만, 시계열 데이터의 노이즈로 인해서 클러스터링을 수행하는 Metric을 설정하는데 제약이 존재한다. 본 논문은 기존 시계열 데이터가 가지고 있는 노이즈를 PIP 기법을 사용하여 제거하고, 노이즈가 없는 시계열 데이터를 클러스터링하기 위한 효율적인 새로운 Metric을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.418-421
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• 2007

In this paper, we proposed a hierarchical nearest-neighbor searching method for deciding fitness of a clustered segment. It is difficult to distinguish the difference between correct spots and atypical noisy spots in footprint patterns. Therefore we could not completely remove unsuitable noisy spots from binarized image in image preprocessing stage or clustering stage. As a preprocessing stage for recognition of insect footprints, this method decides whether a segment is suitable or not, using degree of clustered segment fitness, and then unsuitable segments are eliminated from patterns. Removing unsuitable segments can improve performance of feature extraction for recognition of inset footprints.