• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1029-1035
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• 2013

This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.97-105
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• 2015

Content-adaptive steganography methods embed secret messages in hard-to-model regions of covers such as complicated texture or noisy area. Content-adaptive steganalysis methods often need high dimensional features to capture more subtle relationships of local dependencies among adjacent pixels. However, these methods require many computational complexity and depend on the location of hidden message and the exploited distortion metrics. In this paper, we propose an improved steganalysis method for content-adaptive steganography to enhance detection rate with small number features. We first show that the features form the difference between DCT coefficients are useful for analyzing the content-adaptive steganography methods, and present feature extraction mehtod using first-order Markov probability for the the difference between DCT coefficients. The extracted features are used as input of ensemble classifier. Experimental results show that the proposed method outperforms previous schemes in terms of detection rates and accuracy in spite of a small number features in various content-adaptive stego images.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.154-161
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• 2008

In this paper, a novel method is proposed using color and pattern information for recognizing some emotions included in a fertile. Here we use 10 Kobayashi emotion to represent emotions. - { romantic, clear, natural, casual, elegant chic, dynamic, classic, dandy, modem } The proposed system is composed of feature extraction and classification. To transform the subjective emotions as physical visual features, we extract representative colors and Patterns from textile. Here, the representative color prototypes are extracted by color quantization method, and patterns exacted by wavelet transform followed by statistical analysis. These exacted features are given as input to the neural network (NN)-based classifiers, which decides whether or not a textile had the corresponding emotion. When assessing the effectiveness of the proposed system with 389 textiles collected from various application domains such as interior, fashion, and artificial ones. The results showed that the proposed method has the precision of 100% and the recall of 99%, thereby it can be used in various textile industries.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.3
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• pp.75-84
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• 2005

Since an unexpected fault of induction motor drive systems can cause serious troubles in many industrial applications, which the technique is required to diagnose faults of a voltage-fed PWM inverter for induction motor drives. The considered fault types are rectifier diodes, switching devices and input terminals with open-circuit faults, and the signal for diagnosis is derived from motor currents. The magnitude of dq-current trajectory is used for the feature extraction of a fault and PCA LDA are applied to diagnose. Also, we show results with respect to the execution time because of the possibility to use that a diagnosis software is embedded in the controllers of medium and small size induction motors drive for real-time diagnosis. After we performed various simulations for the fault diagnosis of the inverter, the usefulness of proposed algerian was verified.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.89-101
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• 2008

The most used tool for quality control is control chart in manufacturing industry. But it has limitations at current situation where most of manufacturing facilities are automated and several manufacturing processes have interdependent relationship such as CCM assembly line. To Solve problems, we propose quality management system based on data mining that are consisted of monitoring system where it monitors flows of processes at single window and feature extraction system where it predicts the yield of final product and identifies which processes have impact on the quality of final product. The quality management system uses decision tree, neural network, self-organizing map for data mining. We hope that the proposed system can help manufacturing process to produce stable quality of products and provides engineers useful information such as the predicted yield for current status, identification of causal processes for lots of abnormality.

• Advances in Computational Design
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• v.5 no.2
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• pp.127-146
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• 2020

Building Information Modeling (BIM) is increasingly used throughout the facility's life cycle for various applications, such as design, construction, facility management, and maintenance. For existing buildings, the geometry of as-built BIM is often constructed using dense, three dimensional (3D) point clouds data obtained with laser scanners. Traditionally, as-built BIM systems do not contain the material and textural information of the buildings' elements. This paper presents a semi-automatic method for generation of material and texture rich as-built BIM. The method captures and integrates material and textural information of building elements into as-built BIM using thermal infrared sensing (TIS). The proposed method uses TIS to capture thermal images of the interior walls of an existing building. These images are then processed to extract the interior walls using a segmentation algorithm. The digital numbers in the resulted images are then transformed into radiance values that represent the emitted thermal infrared radiation. Machine learning techniques are then applied to build a correlation between the radiance values and the material type in each image. The radiance values were used to extract textural information from the images. The extracted textural and material information are then robustly integrated into the as-built BIM providing the data needed for the assessment of building conditions in general including energy efficiency, among others.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.60-65
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• 2018

In this paper, a covariance matrix and modulogram are proposed for realizing amphibian sound classification using CNN (Convolutional Neural Network). First of all, a database is established by collecting amphibians sounds including endangered species in natural environment. In order to apply the database to CNN, it is necessary to standardize acoustic signals with different lengths. To standardize the acoustic signals, covariance matrix that gives distribution information and modulogram that contains the information about change over time are extracted and used as input to CNN. The experiment is conducted by varying the number of a convolutional layer and a fully-connected layer. For performance assessment, several conventional methods are considered representing various feature extraction and classification approaches. From the results, it is confirmed that convolutional layer has a greater impact on performance than the fully-connected layer. Also, the performance based on CNN shows attaining the highest recognition rate with 99.07 % among the considered methods.

• International Journal of Oral Biology
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• v.43 no.2
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• pp.101-111
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• 2018

Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.27 no.3
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• pp.271-275
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• 2001

Nine cases of maxillary sinus aspergillosis during a period from February of 1992 to June of 2000 were investigated to analyze the clinical, radiologic and pathologic features. Maxillary sinus aspergillosis is rare disease, but it was increasing tendency with overuse antibiotics, steroid hormones, and anticancer agents. Aspergillosis of the maxillary sinus may occur as a chronic disease in an otherwise healthy person. The clinical features of maxillary sinus aspergillosis were similar to the non-fungal, chronic sinusitis. Intrasinus calcification is known to be a characteristic feature of maxillary sinus aspergillosis. It is suggested that excess root filling materials containing zinc oxide in the maxillary sinus could favour the formation of a local, non-invasive maxillary sinus aspergillosis. And this "dental" model of pathogenensis of maxillary sinus aspergillosis is an alternative to the widely accepted concept of spore inhalation and "aero-genic" pathogenensis of maxillary sinus aspergillosis. The radical surgery such as Caldwell-Luc operation was one of the most effective treatment modalities. Our results of this study indicate that maxillary sinus aspergillosis might occur mainly in healthy individuals rather than debilitating patients. It could efficiently treated with radical surgery alone without the antifungal agents. 4 cases were suspected to be related with teeth extraction and endodontic treatment. There were no recurrence in all cases.

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.383-396
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• 2009

Bone age assessment has been widely used in pediatrics to identify endocrine problems of children. Since the number of trained doctors is far less than the demands, there has been numerous requests for automatic estimation of bone age. Therefore, in this paper, we propose an automatic bone age assessment method that utilizes pattern classification techniques. The proposed method consists of three modules; a finger segmentation module, a normalized shape model generation module and a bone age estimation module. The finger segmentation module segments fingers and epiphyseal regions by means of various image processing algorithms. The shape model abstraction module employ ASM to improves the accuracy of feature extraction for bone age estimation. In addition, SVM is used for estimation of bone age. Features for the estimation include the length of bone and the ratios of bone length. We evaluated the performance of the proposed method through statistical analysis by comparing the bone age assessment results by clinical experts and the proposed automatic method. Through the experimental results, the mean error of the assessment was 0.679 year, which was better than the average error acceptable in clinical practice.