• Journal of Drive and Control
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• v.19 no.3
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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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• v.13 no.2
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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.

• Journal of the Korea Institute of Building Construction
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• v.19 no.5
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• pp.383-389
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• 2019

The supply of natural aggregate for concrete has been difficult, and the amount of construction waste has been continuously increasing. Therefore, the necessity of using recycled aggregate made of construction waste as aggregate is rised. Therefore, many studies on the characteristics of concrete using recycled aggregate have been made and positive studies have been reported mainly in recent studies. A study on the chlorides binding effect of the mortar with recycled coarse aggregate has been reported. However, due to the user's perception of waste, most of the recycled aggregate currently produced is used only for low value-added products. In order to improve the recognition of recycled aggregate and the user's perception of recycled aggregate concrete, long-term monitoring of the structure with 100% recycled aggregate was conducted to confirm the applicability of the recycled aggregate concrete.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.885-892
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• 2004

In this paper, we propose a multi-modal biometrics system based on face and signature recognition system. Here, the face recognition system is designed by fuzzy LDA, and the signature recognition system is implemented with the LDA and segment matching methods. To effectively aggregate two systems, we obtain statistical distribution models based on matching values for genuine and impostor, respectively. And then, the final verification is Performed by the support vector machine. From the various experiments, we find that the proposed method shows high recognition rates comparing with the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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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.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.7
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• pp.459-466
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• 2014

In this paper, we suggest an efficient image processing method for fast paper currency counting with pattern recognition. The patterns are consisted of feature data in each note object extracted from full reflection image of notes and a general contact image sensor(CIS) is used to aggregate the feature images. The proposed pattern recognition algorithm can endure image variation when the paper currency is scanned because it is not sensitive to changes of image resulting in successful note recognition. We tested 100 notes per denomination and currency of several countries including Korea, U.S., China, EU, Britain and Turkey. To ensure the reliability of the result, we tested a total of 10 times per each direction of notes. We can conclude that this algorithm will be applicable to commercial product because of its successful recognition rates. The 100% recognition rates are obtained in almost cases with exceptional case of 99.9% in Euro and 99.8% in Turkish Lira.

• Journal of KIISE:Software and Applications
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• v.34 no.4
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• pp.377-388
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• 2007

In this paper, we propose a multi-modal biometric system based on face, iris and fingerprint recognition system. To effectively aggregate two systems, we use statistical distribution models based on matching values for genuine and impostor, respectively. And then, We performed reveal fusion algorithms including weighted summation, Support Vector Machine(SVM), Fisher discriminant analysis, Bayesian classifier. From the various experiments, we found that the performance of multi-modal biometric system was influenced with the normalization methods and classifiers.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2002.05a
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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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• v.12 no.9
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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.