• 드라이브 ㆍ 컨트롤
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• 제19권3호
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• pp.16-22
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• 2022

Crushers are equipment that crush natural stones, to produce aggregates used at construction sites. As the crusher proceeds, the inner liner becomes worn, causing the size of the aggregate produced to gradually increase. The vision sensor-based aggregate analysis system analyzes the size and distribution of aggregates in production, in real time through image analysis. This study developed an algorithm that can segmentate aggregates in images in real time. using image preprocessing technology combining various filters and morphology techniques, and aggregate region characteristics such as convex hull and concave hull. We applied the developed algorithm to fine aggregate, intermediate aggregate, and thick aggregate images to verify their performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• 한국건축시공학회지
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• 제19권5호
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• pp.383-389
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• 2019

콘크리트용 천연골재의 공급이 어려워지고 건설폐기물은 발생량이 지속적으로 증가하고 있는 추세이다. 따라서 건설폐기물을 골재로 만든 순환골재 사용의 필요성이 대두되고 있다. 이에 순환골재를 사용한 콘크리트의 특성에 대한 많은 연구가 진행되고 있으며 최근 연구들을 중심으로 긍정적인 연구결과들이 보고되고 있다. 그 중 순환굵은골재의 부착모르타르의 염화물고정효과에 관한 연구도 보고되고 있다. 그러나 폐기물이라는 사용자의 인식 때문에 현재 생산되는 순환골재의 대부분이 성 복토용 등 저부가가치용으로만 사용되고 있는 실정이다. 이에 순환골재에 대한 홍보와 순환골재 콘크리트에 대한 사용자의 인식을 개선하기 위해 순환골재를 100% 적용한 구조물의 장기 모니터링을 실시하여 순환골재 콘크리트의 구조체 적용 가능성을 확인했다.

• 정보처리학회논문지B
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• 제11B권7호
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• pp.885-892
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• 2004

본 논문에서는 SVM에 기반을 둔 결정법칙에 의해 얼굴인식과 서명인식시스템으로 구성된 다중생체인식시스템을 제안하고자 한다. 이를 위해 퍼지 선형판별기법(Fuzzy Linear Discriminant Analysis : Fnzzy LDA)를 이용한 얼굴인식과 선형판별분석기법과 구간매칭기법을 이용한 서명인식을 구축하였다. 두 개의 단일생체인식시스템을 효과적으로 융합시키기 위해 우선 독립적인 두 개의 생체인식시스템에 의해 산출된 매칭도로부터 등록자(Genuine)와 침입자(Impostor)의 확률 분포 모델을 생성한 후, SVM(Support Vector Machine)에 의해 최종 인증하는 구조로 되어있다. 제안된 방법인 SVM기반 결정법칙을 적용하여 실험한 결과 기존에 결정법칙으로 많이 사용되고 있는 가중치합과 결정트리 방식에 비해 각각 $1.654{\%}$와 $3.3{\%}$의 인식률 향상을 나타내 제안된 방법의 우수성을 나타냈다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2078-2093
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• 2019

Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 봄 학술발표회 논문집
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• 한국통신학회논문지
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• 제39B권7호
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• pp.459-466
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• 2014

본 논문에서는 권종 인식을 위하여 범용 CIS(contact image sensor)를 사용하여 각 권종별로 취득된 지폐 반사 전체 이미지의 특징 데이터(feature data) 성분을 추출하여 권종 인식의 데이터로 사용함으로써 개별 객체의 특색이나 특징들의 집합인 패턴을 이용한 효과적인 이미지 처리 방법을 제안하였다. 본 논문에서 제안한 방법을 통하여 각 권종별 추출된 이미지의 특징 데이터는 이미지 변화에 덜 민감하면서 공간적인 분포를 잘 나타내기 때문에 권종 인식을 하는데 있어서 우수한 방법이 될 수 있다. 제안된 알고리즘의 테스트를 위하여 시료 진폐는 각 국가 및 권종 당 100매씩을 테스트 하였으며, 제한적인 시료로 인한 판정 결과의 신뢰도를 확보하고자 방향별 총 10회씩 투입하였다. 시험 결과 한국 원화는 100% 인식하였으며, 유로화는 5유로의 경우 99.9%, 20유로의 경우 99.8%의 인식률을 보였으며, 터키 리라화는 20리라의 경우 99.8.%, 50리라의 경우 99.8%의 인식률을 보였고, 나머지 미국 달러화, 중국 위안화, 영국 파운드화 등의 권종은 100% 인식되어 제안된 알고리즘이 상용 제품에 적용 가능함을 보였다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제34권4호
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• pp.377-388
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• 2007

본 연구는 다중 생체 인식 기법을 이용하여 개인 확인 및 인증을 구현한 것으로， 단일생체인식 에서 많이 사용되어 지고 있는 생체 정보 중 얼굴과 지문， 홍채를 이용하여 상호 비교하고 구현하였다. 이를 위한 결합방식으로 단일 생체인식에서 얻은 유사도를 이용하는 방식인 유사도 단계에서의 결합방식을 적용하였으며， 이때의 각 유사도가 동일한 범위가 되도록 하는 여러 가지 균등화 방법에 대하여 연구하였다. 결합방법으로는 가중치 합， Support Vector Machine， Fisher 분류기， 베이시안 분류기를 사용하여 비교하였다. 다양한 실험결과， 사용되는 다중생체인식 조합에 따라 우수한 성능을 보이는 균등화 방법 및 분류기가 다르게 나타남을 알 수 있었다.

• 한국인지과학회:학술대회논문집
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• 한국인지과학회 2002년도 춘계학술대회
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• pp.111-116
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• 2002

The regularity effect for printed word recognition and naming depends on ambiguities between single letters (small grain-size) and their phonemic values. As a given word is repeated and becomes more familiar, letter-aggregate size (grain-size) is predicted to increase, thereby decreasing the ambiguity between spelling pattern and phonological representation and, therefore, decreasing the regularity effect. Lexical decision and naming tasks studied the effect of repetition on the regularity effect for words. The familiarity of a word from was manipulated by presenting low and high frequency words as well as by presenting half the stimuli in mixed upper- and lowercase letters (an unfamiliar form) and half in uniform case. In lexical decision, the regularity effect was initially strong for low frequency words but became null after two presentations; in naming it was also initially strong but was merely reduced (although still substantial) after three repetitions. Mixed case words were recognized and named more slowly and tended to show stronger regularity effects. The results were consistent with the primary hypothesis that familiar word forms are read faster because they are processed at a larger grain-size, which requires fewer operations to achieve lexical selection. Results are discussed in terms of a neurobiological model of word recognition based on brain imaging studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4412-4428
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• 2018

The purpose of this work is to solve the problem of representing an entire video using Convolutional Neural Network (CNN) features for human action recognition. Recently, due to insufficient GPU memory, it has been difficult to take the whole video as the input of the CNN for end-to-end learning. A typical method is to use sampled video frames as inputs and corresponding labels as supervision. One major issue of this popular approach is that the local samples may not contain the information indicated by the global labels and sufficient motion information. To address this issue, we propose a binary hashing method to enhance the local feature extractors. First, we extract the local features and aggregate them into global features using maximum/minimum pooling. Second, we use the binary hashing method to capture the motion features. Finally, we concatenate the hashing features with global features using different normalization methods to train the classifier. Experimental results on the JHMDB and MPII-Cooking datasets show that, for these new local features, binary hashing mapping on the sparsely sampled features led to significant performance improvements.