• Journal of the Korean Society for Precision Engineering
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• v.33 no.2
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• pp.129-138
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• 2016

This paper presents a natural corners-based SLAM (Simultaneous Localization and Mapping) with a robust data association algorithm in a real unknown environment. Corners are extracted from raw laser sensor data, which are chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, local best matching vector between new corners and stored ones is found by joint compatibility, while nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with linear matching time. Finally, SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.265-275
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• 2015

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.828-833
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• 2005

Kernel methods have shown to improve the performance of conventional linear classification algorithms for complex distributed data sets, as mapping the data in input space into a higher dimensional feature space(7). In this paper, we propose a fuzzy kernel K-nearest neighbor(fuzzy kernel K-NN) algorithm, which applies the distance measure in feature space based on kernel functions to the fuzzy K-nearest neighbor(fuzzy K-NN) algorithm. In doing so, the proposed algorithm can enhance the Performance of the conventional algorithm, by choosing an appropriate kernel function. Results on several data sets and segmentation results for real images are given to show the validity of our proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.357-364
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• 2003

National Geography Institute(NGI, National mapping agency) has been producing national basemap in automated process since middle of 1980's toward the systematic and efficient management of national land. In 1995, Korean government initiated a full-scale implementation of the National Geographic Information System(NGIS) Development Plan. Under the NGIS Development Plan, NGI began to produce digital maps in the scales of 1:1,000, 1:5,000, 1:25,000. However, digital maps of 1:250,000 or less scale, which are currently used for national land planning, were not included in NGIS Development Plan. Also, the existing laws and specifications related to digital maps of 1:250,000 or less scale are not clearly defined. Therefore this study proposed a feature classification system, which defines features that should be represented in digital map of 1:250,000 or less scale.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.201-206
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• 1998

In this paper, we have considered the method for clustering land cover types over the East Asia from AVHRR data. The feature vectors such that maximum NDVI, amplitude of NDVI, mean NDVI, and NDVI threshold are extracted from the 10-day composite by maximum value composite(MVC) for reducing the effect of cloud contaninations. To find the land cover clusters given by the feature vectors, we are adapted the self-organizing feature map(SOFM) clustering which is the mapping of an input vector space of n-dimensions into a one - or two-dimensional grid of output layer. The approach is to find first the clusters by the first layer SOFM and then merge several clusters of the first layer to a large cluster by the second layer SOFM. In experiments, we were used the 8-km AVHRR data for two years(1992-1993) over the East Asia.

• Speech Sciences
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• v.14 no.2
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• pp.127-135
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• 2007

Feature vectors of speech are generally extracted from whole frequency domain. The inherent character of a speaker is located in the low band or high band frequency. However, if the speech is corrupted by narrowband noise with concentrated energy, speaker verification performance is reduced as the individual characteristic is removed. In this paper, we propose a PCA Covariance Model based on the multiband to extract the robust feature vectors against the narrowband noise. First, we divide the overall frequency band into several subbands. Second, the correlation of feature vectors extracted independently from each subband is removed by PCA. The distance obtained from each subband has different distribution. To normalize against the different distribution, we moved the value into the normalized distribution through the mapping function. Finally, the represented value applying the weighting function is used for speaker verification. In the experiments, the proposed method shows better performance of the speaker verification and reduces the computation.

• Korean Journal of English Language and Linguistics
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• v.4 no.2
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• pp.195-227
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• 2004

This paper concerns the issue related to the focus phenomena with a particular reference to the two alternating orders (continuous vs. discontinuous orders) in particle constructions in English. To explain the alternation of word order in particle constructions, I will argue that the choice of word order is closely related to the focus property of the object DPs. Following Drubig (2003), I will assume that focus-feature is taken to be a syntactic feature assigned freely to a lexical head in the process of the mapping into Lexical Array (LA) from the lexicon (LEX). I argue that the focus-marked object DP cannot move out of its focus domain and thus the continuous order is derived. In the case of non-focus-marked object DP, however, the object DP moves out of VP in order to receive an appropriate interpretation. As a result, the discontinuous order is derived.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.8-13
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• 2019

Visual place recognition is widely researched area in robotics, as it is one of the elemental requirements for autonomous navigation, simultaneous localization and mapping for mobile robots. However, place recognition in changing environment is a challenging problem since a same place look different according to the time, weather, and seasons. This paper presents a feature extraction method using a deep convolutional auto-encoder to recognize places under severe appearance changes. Given database and query image sequences from different environments, the convolutional auto-encoder is trained to predict the images of the desired environment. The training process is performed by minimizing the loss function between the predicted image and the desired image. After finishing the training process, the encoding part of the structure transforms an input image to a low dimensional latent representation, and it can be used as a condition-invariant feature for recognizing places in changing environment. Experiments were conducted to prove the effective of the proposed method, and the results showed that our method outperformed than existing methods.