• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.4 s.334
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• pp.29-36
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• 2005

This paper presents the first hardware design of ARIA that KSA(Korea Standards Association) decided as the block encryption standard at Dec. 2004. The ARIA cryptographic algorithm has an efficient involution SPN (Substitution Permutation Network) and is immune to known attacks. The proposed ARIA design based on 1 cycle/round include a dual port ROM to reduce a size of circuit md a high speed round key generator with barrel rotator. ARIA design proposed is implemented with Xilinx VirtexE-1600 FPGA. Throughput is 437 Mbps using 1,491 slices and 16 RAM blocks. To demonstrate the ARIA system operation, we developed a security system cyphering video data of communication though Internet. ARIA addresses applications with high-throughput like data storage and internet security protocol (IPSec and TLS) as well as IC cards.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.485-490
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• 2004

In data representation, the clustering performs a grouping process which combines given data into some similar clusters. The various similarity measures have been used in many researches. But, the validity of clustering results is subjective and ambiguous, because of difficulty and shortage about objective criterion of clustering. The fuzzy clustering provides a good method for subjective clustering problems. It performs clustering through the similarity matrix which has fuzzy membership value for assigning each object. In this paper, for objective fuzzy clustering, the clustering algorithm which joins principal components analysis as a dimension reduction model with bayesian learning as a statistical learning theory. For performance evaluation of proposed algorithm, Iris and Glass identification data from UCI Machine Learning repository are used. The experimental results shows a happy outcome of proposed model.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.107-120
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• 2011

Several MODIS AQUA products have been compared with shipboard data to assess the possibility of using remote sensing to estimate particulate organic carbon (POC) concentration in the surface waters of the East Sea. A total of 30 POC profiles obtained in spring and summer seasons of the years of 2006~2010 were compared with remote sensing reflectance at various wavelengths and diffuse attenuation coefficient at 490 nm observed by MODIS AQUA. The algorithm thus established was $POC=266.85^*[R_{rs}(488)/R_{rs}(555)]^{-1.447}$ ($R^2=0.924$) with root mean square error of 20.9 mg $m^{-3}$. Remotely sensed POC contents derived using our algorithm appeared also not to be affected by the presence of non-POC component in suspended particulate matter. Therefore this algorithm could be applied to obtain POC concentration over the East Sea using MODIS Aqua observation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.7
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• pp.479-490
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• 2014

A lower extremity exoskeleton is a robot device that attaches to the lower limbs of the human body to augment or assist with the walking ability of the wearer. In order to improve the wearer's walking ability, the robot senses the wearer's walking locomotion and classifies it into a gait-phase state, after which it drives the appropriate robot motions for each state using its actuators. This paper presents a method by which the robot senses the wearer's locomotion along with a novel classification algorithm which classifies the sensed data as a gait-phase state. The robot determines its control mode using this gait-phase information. If erroneous information is delivered, the robot will fail to improve the walking ability or will bring some discomfort to the wearer. Therefore, it is necessary for the algorithm constantly to classify the correct gait-phase information. However, our device for sensing a human's locomotion has very sensitive characteristics sufficient for it to detect small movements. With only simple logic like a threshold-based classification, it is difficult to deliver the correct information continually. In order to overcome this and provide correct information in a timely manner, a probabilistic gait-phase classification algorithm is proposed. Experimental results demonstrate that the proposed algorithm offers excellent accuracy.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1259-1268
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• 2021

The 2nd Geostationary Ocean Color Imager (GOCI-II) is the successor to the Geostationary Ocean Color Imager (GOCI), which employs one near-ultraviolet wavelength (380 nm) and eight visible wavelengths(412, 443, 490, 510, 555, 620, 660, 680 nm) and three near-infrared wavelengths(709, 745, 865 nm) to observe the marine environment in Northeast Asia, including the Korean Peninsula. However, the multispectral radiance image observed at satellite altitude includes both the water-leaving radiance and the atmospheric path radiance. Therefore, the atmospheric correction process to estimate the water-leaving radiance without the path radiance is essential for analyzing the ocean environment. This manuscript describes the GOCI-II standard atmospheric correction algorithm and its initial phase validation. The GOCI-II atmospheric correction method is theoretically based on the previous GOCI atmospheric correction, then partially improved for turbid water with the GOCI-II's two additional bands, i.e., 620 and 709 nm. The match-up showed an acceptable result, with the mean absolute percentage errors are fall within 5% in blue bands. It is supposed that part of the deviation over case-II waters arose from a lack of near-infrared vicarious calibration. We expect the GOCI-II atmospheric correction algorithm to be improved and updated regularly to the GOCI-II data processing system through continuous calibration and validation activities.

• Journal of the Korea Concrete Institute
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• v.24 no.4
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• pp.481-490
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• 2012

Evaluation of chloride penetration is very important, because induced chloride ion causes corrosion in embedded steel. Diffusion coefficient obtained from rapid chloride penetration test is currently used, however this method cannot provide a correct prediction of chloride content since it shows only ion migration velocity in electrical field. Apparent diffusion coefficient of chloride ion based on simple Fick's Law can provide a total chloride penetration magnitude to engineers. This study proposes an analysis technique to predict chloride penetration using apparent diffusion coefficient of chloride ion from neural network (NN) algorithm and time-dependent diffusion phenomena. For this work, thirty mix proportions with the related diffusion coefficients are studied. The components of mix proportions such as w/b ratio, unit content of cement, slag, fly ash, silica fume, and fine/coarse aggregate are selected as neurons, then learning for apparent diffusion coefficient is trained. Considering time-dependent diffusion coefficient based on Fick's Law, the technique for chloride penetration analysis is proposed. The applicability of the technique is verified through test results from short, long term submerged test, and field investigations. The proposed technique can be improved through NN learning-training based on the acquisition of various mix proportions and the related diffusion coefficients of chloride ion.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.481-490
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• 2013

In this paper, an investigation of weapon tracking performance is shown in regard to improving individual weapon performance of aiming objects. On the battlefield, a battle can last only a few hours, sometimes it can last several days until finished. In these long-lasting combats, a wide variety of factors will gradually lower the visual ability of soldiers. The experiments were focusing on enhancing the degraded aiming performance by applying visual tracking technology to roof mounted sights so as to track the movement of troops automatically. In order to select the optimal algorithm among the latest visual tracking techniques, performance of each algorithm was evaluated using the real combat images with characteristics of overlapping problems, camera's mobility, size changes, low contrast images, and illumination changes. The results show that VTD (Visual Tracking Decomposition)[2], IVT (Incremental learning for robust Visual Tracking)[7], and MIL (Multiple Instance Learning)[1] perform the best at accuracy, response speed, and total performance, respectively. The evaluation suggests that the roof mounted sights equipped with visual tracking technology are likely to improve the reduced aiming ability of forces.

• Journal of KIISE:Software and Applications
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• v.36 no.6
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• pp.479-490
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• 2009

A number of temporal abstraction approaches have been suggested so far to handle the high computational complexity of Markov decision problems (MDPs). Although the structure of temporal abstraction can significantly affect the efficiency of solving the MDP, to our knowledge none of current temporal abstraction approaches explicitly consider the relationship between topology and efficiency. In this paper, we first show that a topological measurement from complex network literature, mean geodesic distance, can reflect the efficiency of solving MDP. Based on this, we build an incremental method to systematically build temporal abstractions using a network model that guarantees a small mean geodesic distance. We test our algorithm on a realistic 3D game environment, and experimental results show that our model has subpolynomial growth of mean geodesic distance according to problem size, which enables efficient solving of resulting MDP.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.483-490
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• 2019

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.485-490
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• 2017

In this paper, we propose a technique for high speed removal of sea-fog using a graphic processor. This technique uses a host processor(CPU) and several graphics processors(GPU) capable of parallel processing to remove sea-fog from the input image. In the process of removing sea-fog, the dark channel extraction, the maximum brightness channel extraction, and the calculation of the transmission are performed by the host processor, and the process of refining the transmission by applying the bidirectional filter is performed in parallel through the graphic processor. To verify the proposed parallel processing method, three NVIDIA GTX 1070 GPUs were used to construct the verification environment. As a result, it takes about 140ms when implemented with one graphics processor, and 26ms when implemented using OpenMP and multiple GPGPUs. The proposed a parallel processing algorithm based on the graphics processor unit can be used for safe navigation, port control and monitoring system.