• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.33-42
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• 2010

This research propose a practical guidance system considering ocean currents in real sea operation. Optimality of generated path is not an issue in this paper. Way-points from start point to possible goal positions are selected by experienced human supervisors considering major ocean current axis. This paper also describes the implementation of a precise underwater navigation solution using multi-sensor fusion technique based on USBL, GPS, DVL and AHRS measurements in detail. To implement the precise, accurate and frequent underwater navigation solution, three strategies are chosen. The first one is the heading alignment angle identification to enhance the performance of standalone dead-reckoning algorithm. The second one is that absolute position is fused timely to prevent accumulation of integration error, where the absolute position can be selected between USBL and GPS considering sensor status. The third one is introduction of effective outlier rejection algorithm. The performance of the developed algorithm is verified with experimental data of mine disposal vehicle and deep-sea ROV.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.79-90
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• 2007

In this paper we proposed and implemented an incident detection model which combines fuzzy algorithm and traffic pattern in order to enhance the efficiency of incident detection for the highways with lamps. Most of the existing algorithms dealt with highways without lamps and can not be used for detecting incidents in the highways with lamps. The data used for model building are traffic volume, occupancy, and speed data. They have been collected by a loop sensor at 5 minutes interval at a point in the Internal Circular Highway of Seoul for the period of 3 months. In this model, the three parameters collected by sensor were fuzzified and combined with the daily traffic pattern of the link. The test of efficiency of the propsed model was performed by comparing the result of proposed model with traditional APID algorithm and fuzzy algorithm without the pattern data respectively. The result showed significant amount of improvement in reducing the false incident detection rate by 18%.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

• Korean Institute of Interior Design Journal
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• v.24 no.2
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• pp.32-41
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• 2015

We live in the era of unlimited design competition. As the importance of design is increasing in all areas including marketing, each country does its best effort on design development. However, the preparation on protecting interior design rights by intellectual property laws(IPLs) has not been enough even though they occupy an important place in the design field. It is not quite easy to make a judgement on the similarity between two images having a single common factor because the factors which are composed of interior design have complicated interactive relations between them. From the IPLs point of view, designs with the similar overall appearance are decided to be similar. Objective evaluation criteria not only for designers but also for design examiners and judges are required in order to protect interior design by the IPLs. The objective of this study is the analysis of the possibility that a computer algorithm method can be useful to decide the similarity of interior design images. According to this study, it is realized that the Img2 which is one of content-based image retrieval computer programs can be utilized to measure the degree of the similarity. The simulation results of three descriptors(CEDD, FCTH, JCD) in the Img2 showed the high degree of similar patterns compared with the results of perceptual judgment by observers. In particular, it was verified that the Img2 has high availability on interior design images with a high score of similarity below 60 which are perceptually judged by observers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.75-80
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• 2017

Recently, there has been growing interest in spatial data that combines information and communication technology with smart cities. The high-precision LiDAR (Light Dectection and Ranging) equipment is mainly used to collect three-dimensional spatial data, and the acquired data is also used to model geographic features and to manage plant construction and cultural heritages which require precision. The LiDAR equipment can collect precise data, but also has limitations because they are expensive and take long time to collect data. On the other hand, in the field of computer vision, research is being conducted on the methods of acquiring image data and performing 3D reconstruction based on image data without expensive equipment. Thus, precise 3D spatial data can be constructed efficiently by collecting and processing image data using CCTVs which are installed as infrastructure facilities in smart cities. However, this method can have an accuracy problem compared to the existing equipment. In this study, experiments were conducted and the results were analyzed to increase the number of extracted matching points by applying the feature-based method and the area-based method in order to improve the precision of 3D spatial data built with image data acquired from stereo CCTVs. For techniques to extract matching points, SIFT algorithm and PATCH algorithm were used. If precise 3D reconstruction is possible using the image data from stereo CCTVs, it will be possible to collect 3D spatial data with low-cost equipment and to collect and build data in real time because image data can be easily acquired through the Web from smart-phones and drones.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.106-112
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• 2015

In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.525-531
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• 2019

Efforts to utilize human gestures to effectively implement a more natural and interactive interface between humans and computers have been ongoing in recent years. In this paper, we propose a new algorithm that accepts consecutive three-dimensional (3D) depth images, defines a hand model, and robustly extracts the human hand region based on six palm joints and 15 finger joints. Then, the 3D depth images are adaptively binarized to exclude non-interest areas, such as the background, and accurately extracts only the hand of the person, which is the area of interest. Experimental results show that the presented algorithm detects only the human hand region 2.4% more accurately than the existing method. The hand region extraction algorithm proposed in this paper is expected to be useful in various practical applications related to computer vision and image processing, such as gesture recognition, virtual reality implementation, 3D motion games, and sign recognition.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.71-78
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• 2022

This paper proposes an algorithm for 3D object detection by processing point cloud data of 3D LiDAR. Unlike 2D LiDAR, 3D LiDAR-based data was too vast and difficult to process in three dimensions. This paper introduces various studies based on 3D LiDAR and describes 3D LiDAR data processing. In this study, we propose a method of processing data of 3D LiDAR using clustering techniques for object detection and design an algorithm that fuses with cameras for clear and accurate 3D object detection. In addition, we study models for clustering 3D LiDAR-based data and study hyperparameter values according to models. When clustering 3D LiDAR-based data, the DBSCAN algorithm showed the most accurate results, and the hyperparameter values of DBSCAN were compared and analyzed. This study will be helpful for object detection research using 3D LiDAR in the future.

• Progress in Medical Physics
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• v.14 no.1
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• pp.15-19
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• 2003

High dose rate (HDR) brachytherapy for treating a cervix carcinoma has become popular, because it eliminates many of the problems associated with conventional brachytherapy. In order to improve the clinical effectiveness with HDR brachytherapy, a dose calculation algorithm, optimization procedures, and image registrations need to be verified by comparing the dose distributions from a planning computer and those from a phantom. In this study, the phantom was fabricated in order to verify the absolute doses and the relative dose distributions. The measured doses from the phantom were then compared with the treatment planning system for the dose verification. The phantom needs to be designed such that the dose distributions can be quantitatively evaluated by utilizing the dosimeters with a high spatial resolution. Therefore, the small size of the thermoluminescent dosimeter (TLD) chips with a dimension of <1/8"and film dosimetry with a spatial resolution of <1mm used to measure the radiation dosages in the phantom. The phantom called a pelvic phantom was made from water and the tissue-equivalent acrylic plates. In order to firmly hold the HDR applicators in the water phantom, the applicators were inserted into the grooves of the applicator holder. The dose distributions around the applicators, such as Point A and B, were measured by placing a series of TLD chips (TLD-to-TLD distance: 5mm) in the three TLD holders, and placing three verification films in the orthogonal planes. This study used a Nucletron Plato treatment planning system and a Microselectron Ir-192 source unit. The results showed good agreement between the treatment plan and measurement. The comparisons of the absolute dose showed agreement within $\pm$4.0 % of the dose at point A and B, and the bladder and rectum point. In addition, the relative dose distributions by film dosimetry and those calculated by the planning computer show good agreement. This pelvic phantom could be a useful to verify the dose calculation algorithm and the accuracy of the image localization algorithm in the high dose rate (HDR) planning computer. The dose verification with film dosimetry and TLD as quality assurance (QA) tools are currently being undertaken in the Catholic University, Seoul, Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1908-1918
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• 2013

Free-view, auto-stereoscopic video service is a next generation broadcasting system which offers a three-dimensional video, images of the various point are needed. This paper proposes a method that parallelizes the algorithm for arbitrary intermediate view-point image fast generation and make it faster using General Propose Graphic Processing Unit(GPGPU) with help of the Compute Unified Device Architecture(CUDA). It uses a parallelized stereo-matching method between the leftmost and the rightmost depth images to obtain disparity information and It use data calculated disparity increment per depth value. The disparity increment is used to find the location in the intermediate view-point image for each depth in the given images. Then, It is eliminate to disocclusions complement each other and remaining holes are filled image using hole-filling method and to get the final intermediate view-point image. The proposed method was implemented and applied to several test sequences. The results revealed that the quality of the generated intermediate view-point image corresponds to 30.47dB of PSNR in average and it takes about 38 frames per second to generate a Full HD intermediate view-point image.